The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it...The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.展开更多
The coronavirus disease(COVID-19)pandemic has affected the lives of social media users in an unprecedentedmanner.They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their l...The coronavirus disease(COVID-19)pandemic has affected the lives of social media users in an unprecedentedmanner.They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of interest.Therefore,understanding location-oriented sentiments about this situation is of prime importance for political leaders,and strategic decision-makers.To this end,we present a new fully automated algorithm based on artificial intelligence(AI),for extraction of location-oriented public sentiments on the COVID-19 situation.We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation,sentiment analysis,location entity detection,and decomposition tree analysis.We deployed fully automated algorithm on live Twitter feed from July 15,2021 and it is still running as of 12 January,2022.The system was evaluated on a limited dataset between July 15,2021 to August 10,2021.During this evaluation timeframe 150,000 tweets were analyzed and our algorithm found that 9,900 tweets contained one or more location entities.In total,13,220 location entities were detected during the evaluation period,and the rates of average precision and recall rate were 0.901 and 0.967,respectively.As of 12 January,2022,the proposed solution has detected 43,169 locations using entity recognition.According to the best of our knowledge,this study is the first to report location intelligence with entity detection,sentiment analysis,and decomposition tree analysis on social media messages related to COVID-19 and has covered the largest set of languages.展开更多
The carbon neutrality of existing internal combustion engines can be significantly enhanced through the use of sustainable e-fuels;thus,their price has to be reduced.Artificial intelligence(AI)offers a promising pathw...The carbon neutrality of existing internal combustion engines can be significantly enhanced through the use of sustainable e-fuels;thus,their price has to be reduced.Artificial intelligence(AI)offers a promising pathway to streamline and accelerate fuel development by enabling faster and more efficient model creation compared to conventional physicochemical simulations.Despite the apparent advantages,state-of-the-art research typically limits the application of AI to basic predictions within narrow operating ranges.This study introduces a novel AIbased fuel design tool capable of accurately predicting detailed engine performance across a broad range of operating conditions,using comprehensive physicochemical fuel properties as input.The proposed approach provides greater detail and precision than existing state-of-the-art methods.Building on a cost-efficient AI development strategy established in our previous work,the tool was constructed using 17 single-output multi-layer perceptron networks.The tool was validated using engine dynamometer measurements with various test fuels,and then it was applied to a fuel optimization task to demonstrate its effectiveness.The results indicate that the tool’s predictions closely match actual engine performance.Specifically,10 out of the 17 models achieved a mean absolute percentage error of<3%.In the optimization scenario,the optimized fuel had a predicted engine operating score of 40.51%,while the actual score was 41.3%,demonstrating the tool’s potential for accurate fuel design.Thus,this novel approach can support the development of low-cost e-fuels,enabling economically viable,carbon-neutral mobility across a wide range of transport applications.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
Autonomous Underwater Vehicles(AUVs)are pivotal for deep-sea exploration and resource exploitation,yet their reliability in extreme underwater environments remains a critical barrier to widespread deployment.Through s...Autonomous Underwater Vehicles(AUVs)are pivotal for deep-sea exploration and resource exploitation,yet their reliability in extreme underwater environments remains a critical barrier to widespread deployment.Through systematic analysis of 150 peer-reviewed studies employing mixed-methods research,this review yields three principal advancements to the reliability analysis of AUVs.First,based on the hierarchical functional division of AUVs into six subsystems(propulsion system,navigation system,communication system,power system,environmental detection system,and emergency system),this study systematically identifies the primary failure modes and potential failure causes of each subsystem,providing theoretical support for fault diagnosis and reliability optimization.Subsequently,a comprehensive review of AUV reliability analysis methods is conducted from three perspectives:analytical methods,simulated methods,and surrogate model methods.The applicability and limitations of each method are critically analyzed to offer insights into their suitability for engineering applications.Finally,the study highlights key challenges and research hotpots in AUV reliability analysis,including reliability analysis under limited data,AI-driven reliability analysis,and human reliability analysis.Furthermore,the potential of multi-sensor data fusion,edge computing,and advanced materials in enhancing AUV environmental adaptability and reliability is explored.展开更多
This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method u...This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data.These representations are then used as input for a support vector machine(SVM)-based metadata classifier,enabling precise detection of attack traffic.This architecture is designed to achieve both high detection accuracy and training efficiency,while adapting flexibly to the diverse service requirements and complexity of 5G network slicing.The model was evaluated using the DDoS Datasets 2022,collected in a simulated 5G slicing environment.Experiments were conducted under both class-balanced and class-imbalanced conditions.In the balanced setting,the model achieved an accuracy of 89.33%,an F1-score of 88.23%,and an Area Under the Curve(AUC)of 89.45%.In the imbalanced setting(attack:normal 7:3),the model maintained strong robustness,=achieving a recall of 100%and an F1-score of 90.91%,demonstrating its effectiveness in diverse real-world scenarios.Compared to existing AI-based detection methods,the proposed model showed higher precision,better handling of class imbalance,and strong generalization performance.Moreover,its modular structure is well-suited for deployment in containerized network function(NF)environments,making it a practical solution for real-world 5G infrastructure.These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks.展开更多
This study explores the application of artificial intelligence-based teaching supervision systems in vocational education,addressing challenges in traditional teaching and supervision.The system leverages real-time mo...This study explores the application of artificial intelligence-based teaching supervision systems in vocational education,addressing challenges in traditional teaching and supervision.The system leverages real-time monitoring,behavior recognition,and data analysis to enhance teaching quality and management efficiency.A case study demonstrates significant improvements in student engagement,discipline,and personalized learning outcomes,with classroom interaction rates increasing by 25%and discipline issues decreasing by 40%.Despite challenges in accuracy,data storage,and ethical concerns,the integration of advanced technologies like virtual reality and blockchain offers promising potential for intelligent,data-driven educational models and quality improvement.展开更多
The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communicati...The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communication(mMTC)—present tremendous challenges to conventional methods of bandwidth allocation.A new deep reinforcement learning-based(DRL-based)bandwidth allocation system for real-time,dynamic management of 5G radio access networks is proposed in this paper.Unlike rule-based and static strategies,the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput,fairness,and compliance with QoS requirements.By using extensive simulations mimicking real-world 5G scenarios,the proposed DRL model outperforms current baselines like Long Short-Term Memory(LSTM),linear regression,round-robin,and greedy algorithms.It attains 90%–95%of the maximum theoretical achievable throughput and nearly twice the conventional equal allocation.It is also shown to react well under delay and reliability constraints,outperforming round-robin(hindered by excessive delay and packet loss)and proving to be more efficient than greedy approaches.In conclusion,the efficiency of DRL in optimizing the allocation of bandwidth is highlighted,and its potential to realize self-optimizing,Artificial Intelligence-assisted(AI-assisted)resource management in 5G as well as upcoming 6G networks is revealed.展开更多
The soil packing,influenced by variations in grain size and the gradation pattern within the soil matrix,plays a crucial role in constituting the mechanical properties of sandy soils.However,previous modeling approach...The soil packing,influenced by variations in grain size and the gradation pattern within the soil matrix,plays a crucial role in constituting the mechanical properties of sandy soils.However,previous modeling approaches have overlooked incorporating the full range of representative parameters to accurately predict the soaked California bearing ratio(CBR_(s))of sandy soils by precisely articulating soil packing in the modeling framework.This study presents an innovative artificial intelligence(AI)-based approach for modeling the CBR_(s)of sandy soils,considering grain size variability meticulously.By synthesizing extensive data from multiple sources,i.e.extensive tailored testing program undertaking multiple tests and extant literature,various modeling techniques including genetic expression programming(GEP),multi-expression programming(MEP),support vector machine(SVM),and multi-linear regression(MLR)are utilized to develop models.The research explores two modeling strategies,namely simplified and composite,with the former incorporating only sieve analysis test parameters,while the latter includes compaction test parameters alongside sieve analysis data.The models'performance is assessed using statistical key performance indicators(KPIs).Results indicate that genetic AI-based algorithms,particularly GEP,outperform SVM and conventional regression techniques,effectively capturing complex relationships between input parameters and CBR_(s).Additionally,the study reveals insights into model performance concerning the number of input parameters,with GEP consistently outperforming other models.External validation and Taylor diagram analysis demonstrate the GEP models'superiority over existing literature models on an independent dataset from the literature.Parametric and sensitivity analyses highlight the intricate relationships between grain sizes and CBR_(s),further emphasizing GEP's efficacy in modeling such complexities.This study contributes to enhancing CBR_(s)modeling accuracy for sandy soils,crucial for pertinent infrastructure design and construction rapidly and cost-effectively.展开更多
Real-time sensory signal monitoring systems are crucial for continuous health tracking and enhancing human-interface technologies in virtual reality/augmented reality applications.Recent advancements in micro/nanofabr...Real-time sensory signal monitoring systems are crucial for continuous health tracking and enhancing human-interface technologies in virtual reality/augmented reality applications.Recent advancements in micro/nanofabrication technologies have enabled wearable and implantable sensors to achieve sufficient sensitivity for measuring subtle sensory signals,while integration with wireless communication technologies allows for real-time monitoring and closed-loop user feedback.However,highly sensitive sensing materials face challenges,as their detection results can easily be altered by external factors such as bending,temperature,and humidity.This review discusses methods for decoupling various stimuli and their applications in human interfaces.We cover the latest advancements in decoupled systems,including the design of sensing materials using micro/nanostructured materials,3-dimensional(3D)sensory system architectures,and Artificial intelligence(AI)-based signal decoupling processing techniques.Additionally,we highlight key applications in robotics,wearable,and implantable health monitoring made possible by these decoupled systems.Finally,we suggest future research directions to address the remaining challenges of developing decoupled artificial sensory systems that are resilient to external stimuli.展开更多
Amidst China's aggressive expansion of its high-speed rail network,the intersection of these lines with seismic fault zones has elevated the risk profile for high-speed rail travel.To counteract the potential dang...Amidst China's aggressive expansion of its high-speed rail network,the intersection of these lines with seismic fault zones has elevated the risk profile for high-speed rail travel.To counteract the potential dangers posed by seismic disturbances,China has introduced a comprehensive high-speed railway earthquake early-warning system.This article presents an in-depth examination of this system,encompassing aspects such as its developmental evolution,architectural design,and pivotal technologies.Furthermore,it ventures into the realm of future enhancements and developmental pathways for the system,fusing emergent findings from earthquake early warning research with advancements in artificial intelligence.展开更多
Recent studies have revealed great functional and structural heterogeneity in the ribbon-type synapses at the basolateral pole of the isopotential inner hair cell(IHC).This feature is believed to be critical for audit...Recent studies have revealed great functional and structural heterogeneity in the ribbon-type synapses at the basolateral pole of the isopotential inner hair cell(IHC).This feature is believed to be critical for audition over a wide dynamic range,but whether the spatial gradient of ribbon morphology is fine-tuned in each IHC and how the mitochondrial network is organized to meet local energy demands of synaptic transmission remain unclear.By means of three-dimensional electron microscopy and artificial intelligence-based algorithms,we demonstrated the cell-wide structural quantification of ribbons and mitochondria in mature mid-cochlear IHCs of mice.We found that adjacent IHCs in staggered pairs differ substantially in cell body shape and ribbon morphology gradient as well as mitochondrial organization.Moreover,our analysis argues for a location-specific arrangement of correlated ribbon and mitochondrial function at the basolateral IHC pole.展开更多
The Data Market Management Strategy project proposes a comprehensive framework to harness AI technologies for optimizing data-driven decision-making processes.This framework,illustrated as an integrated ecosystem,unde...The Data Market Management Strategy project proposes a comprehensive framework to harness AI technologies for optimizing data-driven decision-making processes.This framework,illustrated as an integrated ecosystem,underscores the importance of data and model reuse through a structured marketplace environment.However,challenges such as data standardization,interoperability,and privacy concerns remain prevalent in current data markets.For instance,many data platforms still suffer from data silos and inconsistent metadata standards,making it difficult for researchers to efficiently access and reuse data across sectors.Addressing these issues,the proposed system integrates a data market and a model marketplace,facilitating seamless information exchange through Computing Cloud in Taiwan,China.Within this ecosystem,users can generate new models,upload,and share data,contributing to a dynamic and continuously evolving repository.The system enables users to access diverse datasets via standardized APIs and develop advanced models within modular containers such as Jupyter Notebooks.The model marketplace serves as a critical hub,supporting AI model sharing,refinement,and lifecycle management,fostering an environment where data and models are continuously reused.By emphasizing interdisciplinary collaboration,the framework enhances resource utilization,mitigates redundant efforts,and accelerates the development of novel AI solutions.The proposed approach aligns with global trends in federated learning,data privacy-preserving techniques,and open AI model hubs(e.g.,Hugging Face,TensorFlow Hub),ensuring ethical and secure data practices.Ultimately,the framework promotes scalable AI-powered applications,contributing to a more sustainable future in data management.展开更多
With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comp...With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.展开更多
Tomato(Solanum lycopersicum)and potato(Solanum tuberosum),two integral crops within the nightshade family,are crucial sources of nutrients and serve as staple foods worldwide.Molecular genetic studies have significant...Tomato(Solanum lycopersicum)and potato(Solanum tuberosum),two integral crops within the nightshade family,are crucial sources of nutrients and serve as staple foods worldwide.Molecular genetic studies have significantly advanced our understanding of their domestication,evolution,and the establishment of key agronomic traits.Recent studies have revealed that epigenetic modifications act as"molecular switches",crucially regulating phenotypic variations essential for traits such as fruit ripening in tomatoes and tuberization in potatoes.This review summarizes the latest findings on the regulatory mechanisms of epigenetic modifications in these crops and discusses the integration of biotechnology and epigenomics to enhance breeding strategies.By highlighting the role of epigenetic control in augmenting crop yield and adaptation,we underscores its potential to address the challenges posed by a growing global population as well as changing climate.展开更多
Recent studies show that artificial intelligence(AI),such as machine learning and deep learning,models can be adopted and have advantages in fault detection and diagnosis for building energy systems.This paper aims to...Recent studies show that artificial intelligence(AI),such as machine learning and deep learning,models can be adopted and have advantages in fault detection and diagnosis for building energy systems.This paper aims to conduct a comprehensive and systematic literature review on fault detection and diagnosis(FDD)methods for heating,ventilation,and air conditioning(HVAC)systems.This review covers the period from 2013 to 2023 to identify and analyze the existing research in this field.Our work concentrates explicitly on synthesizing AI-based FDD techniques,particularly summarizing these methods and offering a comprehensive classification.First,we discuss the challenges while developing FDD methods for HVAC systems.Next,we classify AI-based FDD methods into three categories:those based on traditional machine learning,deep learning,and hybrid AI models.Additionally,we also examine physical model-based methods to compare them with AI-based methods.The analysis concludes that AI-based HVAC FDD,despite its higher accuracy and reduced reliance on expert knowledge,has garnered considerable research interest compared to physics-based methods.However,it still encounters difficulties in dynamic and time-varying environments and achieving FDD resolution.Addressing these challenges is essential to facilitate the widespread adoption of AI-based FDD in HVAC.展开更多
Design of floating offshore wind turbines(FOWTs)needs reliable and innovative technologies to overcome the challenges on how to better predict the dynamic responses in terms of aero-hydro-servo-elastic disciplines.Thi...Design of floating offshore wind turbines(FOWTs)needs reliable and innovative technologies to overcome the challenges on how to better predict the dynamic responses in terms of aero-hydro-servo-elastic disciplines.This paper aims to demonstrate the optimized prediction of the dynamic response of FOWTs by Simulation annealing diagnosis algorithm(SADA).SADA is an Artificial Intelligence technology-based method,which utilizes the advantages of numerical simulation,basin experiment and machine learning algorithms.The actor network in deep deterministic policy gradient(DDPG)is adopted to take actions to adjust the Key disciplinary parameters(KDPs)in each loop according to the feedback of 6DOF motions of platform in dynamic response analysis.The results demonstrated that the mean values of the platform's motions and rotor axial thrust force could be predicted with higher accuracy.On this basis,other physical quantities that designers are more concerned about but cannot be obtained from experiments and actual measurements will be predicted by SADA with more credibility.This SADA method differs from traditional supervised learning applications in renewable energy,which do not need to be provided physical quantities with strong direct correlation.All targets can be artificially set for SADA to obtain a better self-learning performance.In general,designers can use SADA to get a more accurate and optimized prediction of the dynamic response of FOWTs,especially those physical quantities that cannot be directly obtained through the basin experiments.展开更多
基金funded by the Ministry of Science and Technology,Taiwan,grant number(MOST 111-2221-E167-025-MY2).
文摘The Internet of Everything(IoE)coupled with Proactive Artificial Intelligence(AI)-Based Learning Agents(PLAs)through a cloud processing system is an idea that connects all computing resources to the Internet,making it possible for these devices to communicate with one another.Technologies featured in the IoE include embedding,networking,and sensing devices.To achieve the intended results of the IoE and ease life for everyone involved,sensing devices and monitoring systems are linked together.The IoE is used in several contexts,including intelligent cars’protection,navigation,security,and fuel efficiency.The Smart Things Monitoring System(STMS)framework,which has been proposed for early occurrence identification and theft prevention,is discussed in this article.The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe,control,and monitor vehicles.The STMS is familiar with the platform used by the global positioning system;as a result,the STMS can maintain a real-time record of current vehicle positions.This information is utilized to locate the vehicle in an accident or theft.The findings of the STMS system are promising for precisely identifying crashes,evaluating incident severity,and locating vehicles after collisions have occurred.Moreover,we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters,such as round-trip time(RTT),data packet transmission,data packet reception,and loss.From our experimentation,we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches.Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.
文摘The coronavirus disease(COVID-19)pandemic has affected the lives of social media users in an unprecedentedmanner.They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of interest.Therefore,understanding location-oriented sentiments about this situation is of prime importance for political leaders,and strategic decision-makers.To this end,we present a new fully automated algorithm based on artificial intelligence(AI),for extraction of location-oriented public sentiments on the COVID-19 situation.We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation,sentiment analysis,location entity detection,and decomposition tree analysis.We deployed fully automated algorithm on live Twitter feed from July 15,2021 and it is still running as of 12 January,2022.The system was evaluated on a limited dataset between July 15,2021 to August 10,2021.During this evaluation timeframe 150,000 tweets were analyzed and our algorithm found that 9,900 tweets contained one or more location entities.In total,13,220 location entities were detected during the evaluation period,and the rates of average precision and recall rate were 0.901 and 0.967,respectively.As of 12 January,2022,the proposed solution has detected 43,169 locations using entity recognition.According to the best of our knowledge,this study is the first to report location intelligence with entity detection,sentiment analysis,and decomposition tree analysis on social media messages related to COVID-19 and has covered the largest set of languages.
基金supported by the European Union within the framework of the National Laboratory for Autonomous Systems(RRF-2.3.1-21-2022-00002)supported by AVL Hungary Kft.
文摘The carbon neutrality of existing internal combustion engines can be significantly enhanced through the use of sustainable e-fuels;thus,their price has to be reduced.Artificial intelligence(AI)offers a promising pathway to streamline and accelerate fuel development by enabling faster and more efficient model creation compared to conventional physicochemical simulations.Despite the apparent advantages,state-of-the-art research typically limits the application of AI to basic predictions within narrow operating ranges.This study introduces a novel AIbased fuel design tool capable of accurately predicting detailed engine performance across a broad range of operating conditions,using comprehensive physicochemical fuel properties as input.The proposed approach provides greater detail and precision than existing state-of-the-art methods.Building on a cost-efficient AI development strategy established in our previous work,the tool was constructed using 17 single-output multi-layer perceptron networks.The tool was validated using engine dynamometer measurements with various test fuels,and then it was applied to a fuel optimization task to demonstrate its effectiveness.The results indicate that the tool’s predictions closely match actual engine performance.Specifically,10 out of the 17 models achieved a mean absolute percentage error of<3%.In the optimization scenario,the optimized fuel had a predicted engine operating score of 40.51%,while the actual score was 41.3%,demonstrating the tool’s potential for accurate fuel design.Thus,this novel approach can support the development of low-cost e-fuels,enabling economically viable,carbon-neutral mobility across a wide range of transport applications.
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 National Key R&D Program Projects(Grant No.2022YFC2803601)the Natural Science Foundation of Shandong Province(Grant No.ZR2021YQ29)+1 种基金the Natural Science Foundation of Heilongjiang Province(Grant No.YQ2024E036)the Taishan Scholars Project(Grant No.tsqn202312317).
文摘Autonomous Underwater Vehicles(AUVs)are pivotal for deep-sea exploration and resource exploitation,yet their reliability in extreme underwater environments remains a critical barrier to widespread deployment.Through systematic analysis of 150 peer-reviewed studies employing mixed-methods research,this review yields three principal advancements to the reliability analysis of AUVs.First,based on the hierarchical functional division of AUVs into six subsystems(propulsion system,navigation system,communication system,power system,environmental detection system,and emergency system),this study systematically identifies the primary failure modes and potential failure causes of each subsystem,providing theoretical support for fault diagnosis and reliability optimization.Subsequently,a comprehensive review of AUV reliability analysis methods is conducted from three perspectives:analytical methods,simulated methods,and surrogate model methods.The applicability and limitations of each method are critically analyzed to offer insights into their suitability for engineering applications.Finally,the study highlights key challenges and research hotpots in AUV reliability analysis,including reliability analysis under limited data,AI-driven reliability analysis,and human reliability analysis.Furthermore,the potential of multi-sensor data fusion,edge computing,and advanced materials in enhancing AUV environmental adaptability and reliability is explored.
基金supported by an Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korean government(MSIT)(RS-2024-00438156,Development of Security Resilience Technology Based on Network Slicing Services in a 5G Specialized Network).
文摘This study proposes an efficient traffic classification model to address the growing threat of distributed denial-of-service(DDoS)attacks in 5th generation technology standard(5G)slicing networks.The proposed method utilizes an ensemble of encoder components from multiple autoencoders to compress and extract latent representations from high-dimensional traffic data.These representations are then used as input for a support vector machine(SVM)-based metadata classifier,enabling precise detection of attack traffic.This architecture is designed to achieve both high detection accuracy and training efficiency,while adapting flexibly to the diverse service requirements and complexity of 5G network slicing.The model was evaluated using the DDoS Datasets 2022,collected in a simulated 5G slicing environment.Experiments were conducted under both class-balanced and class-imbalanced conditions.In the balanced setting,the model achieved an accuracy of 89.33%,an F1-score of 88.23%,and an Area Under the Curve(AUC)of 89.45%.In the imbalanced setting(attack:normal 7:3),the model maintained strong robustness,=achieving a recall of 100%and an F1-score of 90.91%,demonstrating its effectiveness in diverse real-world scenarios.Compared to existing AI-based detection methods,the proposed model showed higher precision,better handling of class imbalance,and strong generalization performance.Moreover,its modular structure is well-suited for deployment in containerized network function(NF)environments,making it a practical solution for real-world 5G infrastructure.These results highlight the potential of the proposed approach to enhance both the security and operational resilience of 5G slicing networks.
基金2023 Education and Teaching Research Projects of China“Construction Education Association-Exploration and Practice of Digital Talent Cultivation for Intelligent Buildings Oriented towards China-ASEAN”(2023265)2024 Education and Teaching Reform Research Project of Guangxi Water Resources and Electric Power Vocational and Technical College“Exploration and Research on the Training Model of Innovative Digital Building Talents for China-ASEAN”(2024jgyb19)。
文摘This study explores the application of artificial intelligence-based teaching supervision systems in vocational education,addressing challenges in traditional teaching and supervision.The system leverages real-time monitoring,behavior recognition,and data analysis to enhance teaching quality and management efficiency.A case study demonstrates significant improvements in student engagement,discipline,and personalized learning outcomes,with classroom interaction rates increasing by 25%and discipline issues decreasing by 40%.Despite challenges in accuracy,data storage,and ethical concerns,the integration of advanced technologies like virtual reality and blockchain offers promising potential for intelligent,data-driven educational models and quality improvement.
文摘The explosive growth of data traffic and heterogeneous service requirements of 5G networks—covering Enhanced Mobile Broadband(eMBB),Ultra-Reliable Low Latency Communication(URLLC),and Massive Machine Type Communication(mMTC)—present tremendous challenges to conventional methods of bandwidth allocation.A new deep reinforcement learning-based(DRL-based)bandwidth allocation system for real-time,dynamic management of 5G radio access networks is proposed in this paper.Unlike rule-based and static strategies,the proposed system dynamically updates itself according to shifting network conditions such as traffic load and channel conditions to maximize the achievable throughput,fairness,and compliance with QoS requirements.By using extensive simulations mimicking real-world 5G scenarios,the proposed DRL model outperforms current baselines like Long Short-Term Memory(LSTM),linear regression,round-robin,and greedy algorithms.It attains 90%–95%of the maximum theoretical achievable throughput and nearly twice the conventional equal allocation.It is also shown to react well under delay and reliability constraints,outperforming round-robin(hindered by excessive delay and packet loss)and proving to be more efficient than greedy approaches.In conclusion,the efficiency of DRL in optimizing the allocation of bandwidth is highlighted,and its potential to realize self-optimizing,Artificial Intelligence-assisted(AI-assisted)resource management in 5G as well as upcoming 6G networks is revealed.
文摘The soil packing,influenced by variations in grain size and the gradation pattern within the soil matrix,plays a crucial role in constituting the mechanical properties of sandy soils.However,previous modeling approaches have overlooked incorporating the full range of representative parameters to accurately predict the soaked California bearing ratio(CBR_(s))of sandy soils by precisely articulating soil packing in the modeling framework.This study presents an innovative artificial intelligence(AI)-based approach for modeling the CBR_(s)of sandy soils,considering grain size variability meticulously.By synthesizing extensive data from multiple sources,i.e.extensive tailored testing program undertaking multiple tests and extant literature,various modeling techniques including genetic expression programming(GEP),multi-expression programming(MEP),support vector machine(SVM),and multi-linear regression(MLR)are utilized to develop models.The research explores two modeling strategies,namely simplified and composite,with the former incorporating only sieve analysis test parameters,while the latter includes compaction test parameters alongside sieve analysis data.The models'performance is assessed using statistical key performance indicators(KPIs).Results indicate that genetic AI-based algorithms,particularly GEP,outperform SVM and conventional regression techniques,effectively capturing complex relationships between input parameters and CBR_(s).Additionally,the study reveals insights into model performance concerning the number of input parameters,with GEP consistently outperforming other models.External validation and Taylor diagram analysis demonstrate the GEP models'superiority over existing literature models on an independent dataset from the literature.Parametric and sensitivity analyses highlight the intricate relationships between grain sizes and CBR_(s),further emphasizing GEP's efficacy in modeling such complexities.This study contributes to enhancing CBR_(s)modeling accuracy for sandy soils,crucial for pertinent infrastructure design and construction rapidly and cost-effectively.
基金funding from the Alchemist Project Program(Grant No.RS-2024-00422269)Technology Innovation Program(Grant No.RS-2024-00443121)+1 种基金supported by the Ministry of Trade,Industry&Energy(MOTIE,Korea)support by a National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIP,Ministry of Science,ICT&Future Planning,Grant Nos.NRF-2022R1A4A3032913 and RS-2024-00411904).
文摘Real-time sensory signal monitoring systems are crucial for continuous health tracking and enhancing human-interface technologies in virtual reality/augmented reality applications.Recent advancements in micro/nanofabrication technologies have enabled wearable and implantable sensors to achieve sufficient sensitivity for measuring subtle sensory signals,while integration with wireless communication technologies allows for real-time monitoring and closed-loop user feedback.However,highly sensitive sensing materials face challenges,as their detection results can easily be altered by external factors such as bending,temperature,and humidity.This review discusses methods for decoupling various stimuli and their applications in human interfaces.We cover the latest advancements in decoupled systems,including the design of sensing materials using micro/nanostructured materials,3-dimensional(3D)sensory system architectures,and Artificial intelligence(AI)-based signal decoupling processing techniques.Additionally,we highlight key applications in robotics,wearable,and implantable health monitoring made possible by these decoupled systems.Finally,we suggest future research directions to address the remaining challenges of developing decoupled artificial sensory systems that are resilient to external stimuli.
基金supported by the Science and Technology Development Plan of China State Railway Group Co.,Ltd.(J2022G007)China Academy of Railway Sciences Group Co.,Ltd.(2023YJ103).
文摘Amidst China's aggressive expansion of its high-speed rail network,the intersection of these lines with seismic fault zones has elevated the risk profile for high-speed rail travel.To counteract the potential dangers posed by seismic disturbances,China has introduced a comprehensive high-speed railway earthquake early-warning system.This article presents an in-depth examination of this system,encompassing aspects such as its developmental evolution,architectural design,and pivotal technologies.Furthermore,it ventures into the realm of future enhancements and developmental pathways for the system,fusing emergent findings from earthquake early warning research with advancements in artificial intelligence.
基金the National Natural Science Foundation of China(81800901)the Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning(QD2018015)+2 种基金the Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases(14DZ2260300)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB32030200)the Bureau of International Cooperation,Chinese Academy of Sciences(153D31KYSB20170059).
文摘Recent studies have revealed great functional and structural heterogeneity in the ribbon-type synapses at the basolateral pole of the isopotential inner hair cell(IHC).This feature is believed to be critical for audition over a wide dynamic range,but whether the spatial gradient of ribbon morphology is fine-tuned in each IHC and how the mitochondrial network is organized to meet local energy demands of synaptic transmission remain unclear.By means of three-dimensional electron microscopy and artificial intelligence-based algorithms,we demonstrated the cell-wide structural quantification of ribbons and mitochondria in mature mid-cochlear IHCs of mice.We found that adjacent IHCs in staggered pairs differ substantially in cell body shape and ribbon morphology gradient as well as mitochondrial organization.Moreover,our analysis argues for a location-specific arrangement of correlated ribbon and mitochondrial function at the basolateral IHC pole.
基金the Science Council in Taiwan, China for their support and funding of this project (Grant number: 113-2221-E-492-016)
文摘The Data Market Management Strategy project proposes a comprehensive framework to harness AI technologies for optimizing data-driven decision-making processes.This framework,illustrated as an integrated ecosystem,underscores the importance of data and model reuse through a structured marketplace environment.However,challenges such as data standardization,interoperability,and privacy concerns remain prevalent in current data markets.For instance,many data platforms still suffer from data silos and inconsistent metadata standards,making it difficult for researchers to efficiently access and reuse data across sectors.Addressing these issues,the proposed system integrates a data market and a model marketplace,facilitating seamless information exchange through Computing Cloud in Taiwan,China.Within this ecosystem,users can generate new models,upload,and share data,contributing to a dynamic and continuously evolving repository.The system enables users to access diverse datasets via standardized APIs and develop advanced models within modular containers such as Jupyter Notebooks.The model marketplace serves as a critical hub,supporting AI model sharing,refinement,and lifecycle management,fostering an environment where data and models are continuously reused.By emphasizing interdisciplinary collaboration,the framework enhances resource utilization,mitigates redundant efforts,and accelerates the development of novel AI solutions.The proposed approach aligns with global trends in federated learning,data privacy-preserving techniques,and open AI model hubs(e.g.,Hugging Face,TensorFlow Hub),ensuring ethical and secure data practices.Ultimately,the framework promotes scalable AI-powered applications,contributing to a more sustainable future in data management.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509Development of security monitoring technology based network behavior against encrypted cyber threats in ICT convergence environment).
文摘With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.
基金supported by the National Natural Science Foundation of China(32301896,31991181)the Guangdong Major Project of Basic and Applied Basic Research(2021B0301030004)+1 种基金the Agricultural Science and Technology Innovation Program(CAAS-ZDRW202404)the China Postdoctoral Science Foundation(2023M733834).
文摘Tomato(Solanum lycopersicum)and potato(Solanum tuberosum),two integral crops within the nightshade family,are crucial sources of nutrients and serve as staple foods worldwide.Molecular genetic studies have significantly advanced our understanding of their domestication,evolution,and the establishment of key agronomic traits.Recent studies have revealed that epigenetic modifications act as"molecular switches",crucially regulating phenotypic variations essential for traits such as fruit ripening in tomatoes and tuberization in potatoes.This review summarizes the latest findings on the regulatory mechanisms of epigenetic modifications in these crops and discusses the integration of biotechnology and epigenomics to enhance breeding strategies.By highlighting the role of epigenetic control in augmenting crop yield and adaptation,we underscores its potential to address the challenges posed by a growing global population as well as changing climate.
文摘Recent studies show that artificial intelligence(AI),such as machine learning and deep learning,models can be adopted and have advantages in fault detection and diagnosis for building energy systems.This paper aims to conduct a comprehensive and systematic literature review on fault detection and diagnosis(FDD)methods for heating,ventilation,and air conditioning(HVAC)systems.This review covers the period from 2013 to 2023 to identify and analyze the existing research in this field.Our work concentrates explicitly on synthesizing AI-based FDD techniques,particularly summarizing these methods and offering a comprehensive classification.First,we discuss the challenges while developing FDD methods for HVAC systems.Next,we classify AI-based FDD methods into three categories:those based on traditional machine learning,deep learning,and hybrid AI models.Additionally,we also examine physical model-based methods to compare them with AI-based methods.The analysis concludes that AI-based HVAC FDD,despite its higher accuracy and reduced reliance on expert knowledge,has garnered considerable research interest compared to physics-based methods.However,it still encounters difficulties in dynamic and time-varying environments and achieving FDD resolution.Addressing these challenges is essential to facilitate the widespread adoption of AI-based FDD in HVAC.
文摘Design of floating offshore wind turbines(FOWTs)needs reliable and innovative technologies to overcome the challenges on how to better predict the dynamic responses in terms of aero-hydro-servo-elastic disciplines.This paper aims to demonstrate the optimized prediction of the dynamic response of FOWTs by Simulation annealing diagnosis algorithm(SADA).SADA is an Artificial Intelligence technology-based method,which utilizes the advantages of numerical simulation,basin experiment and machine learning algorithms.The actor network in deep deterministic policy gradient(DDPG)is adopted to take actions to adjust the Key disciplinary parameters(KDPs)in each loop according to the feedback of 6DOF motions of platform in dynamic response analysis.The results demonstrated that the mean values of the platform's motions and rotor axial thrust force could be predicted with higher accuracy.On this basis,other physical quantities that designers are more concerned about but cannot be obtained from experiments and actual measurements will be predicted by SADA with more credibility.This SADA method differs from traditional supervised learning applications in renewable energy,which do not need to be provided physical quantities with strong direct correlation.All targets can be artificially set for SADA to obtain a better self-learning performance.In general,designers can use SADA to get a more accurate and optimized prediction of the dynamic response of FOWTs,especially those physical quantities that cannot be directly obtained through the basin experiments.
