The advancement of generative AI has reshaped EFL education,particularly in EFL writing.This qualitative case study investigates the perceptions of Chinese college students and EFL teachers towards the integration of ...The advancement of generative AI has reshaped EFL education,particularly in EFL writing.This qualitative case study investigates the perceptions of Chinese college students and EFL teachers towards the integration of Gen AI in EFL writing.The research involved semi-structured interviews with 13 students and 10 EFL teachers.Thematic analysis,guided by the Technology Acceptance Model(TAM),was employed to analyze the qualitative data.The findings reveal the perceptions of students and teachers regarding the role of generative AI in EFL writing.Regarding usefulness,students appreciate Gen AI for reducing writing difficulty and enhancing efficiency,though some note that it may produce logical flaws and misinformation.Teachers share similar perceptions,but stress effectiveness depends on students’language level.Some teachers also advocate traditional writing initially to build foundational skills.On the ease of use,most students find it easy interacting with Gen AI but mention dialogical understanding challenges.Both students and teachers stress clear prompts are crucial,indicating“AI interaction literacy”should be part of teaching.Moreover,teachers worry that Gen AI’s ease of use may lead to over-reliance.These results reveal contradicting goals of using Gen AI:students value efficiency,while teachers focus on ability cultivation.These insights guide more effective integration of Gen AI in EFL writing education.展开更多
El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been develope...El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict it.In some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of research.This study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as ICM-UNet.The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.In the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO events.These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies.Furthermore,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.展开更多
This article explores in-class practice of blended teaching of Chinese-English(C-E)translation for English as a Foreign Language(EFL)majors in the era of artificial intelligence(AI).It examines the opportunities and c...This article explores in-class practice of blended teaching of Chinese-English(C-E)translation for English as a Foreign Language(EFL)majors in the era of artificial intelligence(AI).It examines the opportunities and challenges AI presents in enhancing translation education,particularly in fostering student engagement,improving teaching efficiency,and promoting self-motivated learning.Case study suggests that AI can enhance the flexibility of teaching and motivate students,yet challenges such as over-reliance on AI and diminished critical thinking need to be addressed.While acknowledging the indispensability of human translators,the article concludes that effective blended teaching requires purposeful curriculum design,proper integration of AI,and a collaborative effort of teachers and students to maximize the potential of AI while ensuring high-quality,independent learning outcomes.展开更多
Artificial intelligence(AI)has transformed agricultural genetics,especially in the context of crop improvement strategies.Traditional breeding faces challenges such as polyploidy,high level of genomic heterogeneity,an...Artificial intelligence(AI)has transformed agricultural genetics,especially in the context of crop improvement strategies.Traditional breeding faces challenges such as polyploidy,high level of genomic heterogeneity,and complex gene-trait associations.By combining multi-omics data researchers learn more about the genetic and molecular basis of important agricultural traits.However,statistical methods are often insufficient to address the data complexity.By contrast,AI techniques,such as machine learning(ML)and deep learning(DL),are emerging as powerful tools to explore complexity.Algorithms such as random forests(RF)and support vector machines(SVM)can support genomic selection(GS)and trait value prediction.Furthermore,DL models such as convolutional neural networks(CNN)and long short-term memory networks(LSTM)dominate high-throughput phenotyping and time series analyses,providing accurate predictions for crop yield,disease resistance,and genotype adaptation.Large language models(LLMs)are able to integrate complex omics data.AI models can analyze large dataset,generated by genomics,transcriptomics,proteomics,metabolomics,and phenomic applications because algorithms can combine different inputs,such as DNA sequences,gene expression profiles,protein–protein interaction networks,metabolite concentrations,and phenotypic data under specific environmental conditions.The integration of individual models can improve prediction accuracy by reducing resource inputs and automating labor-intensive tasks involved in breeding programs.Some recent AI methods,such as gradient boosting machines(GBMs)and Transformer models,are increasingly being used to improve scalability and accuracy of predictive analytics.This review summarizes major advances in AI applications in agricultural genetics,highlighting the strengths and limitations of different ML and DL models and their role in integrating complex datasets.The study highlights the importance of artificial intelligence in understanding genomic complexity and promoting the development of innovative methods to improve crop performance.展开更多
Satellite communications, pivotal for global connectivity, are increasingly converging with cutting-edge mobile networks, notably 5G, B5G, and 6G. This amalgamation heralds the promise of universal, high-velocity comm...Satellite communications, pivotal for global connectivity, are increasingly converging with cutting-edge mobile networks, notably 5G, B5G, and 6G. This amalgamation heralds the promise of universal, high-velocity communication, yet it is not without its challenges. Paramount concerns encompass spectrum allocation, the harmonization of network architectures, and inherent latency issues in satellite transmissions. Potential mitigations, such as dynamic spectrum sharing and the deployment of edge computing, are explored as viable solutions. Looking ahead, the advent of quantum communications within satellite frameworks and the integration of AI spotlight promising research trajectories. These advancements aim to foster a seamless and synergistic coexistence between satellite communications and next-gen mobile networks.展开更多
The integration of artificial intelligence(AI)in aquaculture has been identified as a transformative force,enhancing various operational aspects from water quality management to genetic optimization.This review provid...The integration of artificial intelligence(AI)in aquaculture has been identified as a transformative force,enhancing various operational aspects from water quality management to genetic optimization.This review provides a comprehensive synthesis of recent advancements in AI applications within the aquaculture sector,underscoring the significant enhancements in production efficiency and environmental sustainability.Key AI-driven improvements,such as predictive analytics for disease management and optimized feeding protocols,are highlighted,demonstrating their contributions to reducing waste and improving biomass outputs.However,challenges remain in terms of data quality,system integration,and the socio-economic impacts of technological adoption across diverse aquacultural environments.This review also addresses the gaps in current research,particularly the lack of robust,scalable AI models and frameworks that can be universally applied.Future directions are discussed,emphasizing the need for interdisciplinary research and development to fully leverage AI potential in aquaculture.This study not only maps the current landscape of AI applications but also serves as a call for continued innovation and strategic collaborations to overcome existing barriers and realize the full benefits of AI in aquaculture.展开更多
The intersection of artificial intelligence(AI)and software engineering marks a transformative phase in the technology industry.This paper delves into AI-driven software engineering,exploring its methodologies,implica...The intersection of artificial intelligence(AI)and software engineering marks a transformative phase in the technology industry.This paper delves into AI-driven software engineering,exploring its methodologies,implications,challenges,and benefits.Drawing from data sources such as GitHub and Bitbucket and insights from industry experts,the study offers a comprehensive view of the current landscape.While the results indicate a promising uptrend in the integration of AI techniques in software development,challenges like model interpretability,ethical concerns,and integration complexities emerge as significant.Nevertheless,the transformative potential of AI within software engineering is profound,ushering in new paradigms of efficiency,innovation,and user experience.The study concludes by emphasizing the need for further research,better tooling,ethical guidelines,and education to fully harness the potential of AI-driven software engineering.展开更多
Embodied intelligence emphasizes the synergy between body,mind,and environment,offering a powerful framework for building more adaptive and interactive intelligent systems.Over the past decade,rapid advancements in ar...Embodied intelligence emphasizes the synergy between body,mind,and environment,offering a powerful framework for building more adaptive and interactive intelligent systems.Over the past decade,rapid advancements in artificial intelligence have driven remarkable achievements in perception,planning,and control tasks,particularly through the rise of deep learning.However,embodied intelligence–the integration of AI with a physical body interacting in real environments–remains a relatively underexplored frontier.Unlike disembodied systems that rely solely on static datasets,embodied agents learn through real-time interaction with their surroundings,leveraging perception-action loops to adaptively understand and manipulate the world.Inspired by human cognition,embodied intelligence emphasizes learning by doing,thereby offering the potential to generalize knowledge across tasks,environments,and sensorimotor experiences.展开更多
文摘The advancement of generative AI has reshaped EFL education,particularly in EFL writing.This qualitative case study investigates the perceptions of Chinese college students and EFL teachers towards the integration of Gen AI in EFL writing.The research involved semi-structured interviews with 13 students and 10 EFL teachers.Thematic analysis,guided by the Technology Acceptance Model(TAM),was employed to analyze the qualitative data.The findings reveal the perceptions of students and teachers regarding the role of generative AI in EFL writing.Regarding usefulness,students appreciate Gen AI for reducing writing difficulty and enhancing efficiency,though some note that it may produce logical flaws and misinformation.Teachers share similar perceptions,but stress effectiveness depends on students’language level.Some teachers also advocate traditional writing initially to build foundational skills.On the ease of use,most students find it easy interacting with Gen AI but mention dialogical understanding challenges.Both students and teachers stress clear prompts are crucial,indicating“AI interaction literacy”should be part of teaching.Moreover,teachers worry that Gen AI’s ease of use may lead to over-reliance.These results reveal contradicting goals of using Gen AI:students value efficiency,while teachers focus on ability cultivation.These insights guide more effective integration of Gen AI in EFL writing education.
基金supported by the National Natural Science Foundation of China(NFSCGrant No.42030410)+2 种基金Laoshan Laboratory(No.LSKJ202202402)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB40000000)the Startup Foundation for Introducing Talent of NUIST.
文摘El Niño-Southern Oscillation(ENSO)is the strongest interannual climate mode influencing the coupled ocean-atmosphere system in the tropical Pacific,and numerous dynamical and statistical models have been developed to simulate and predict it.In some simplified coupled ocean-atmosphere models,the relationship between sea surface temperature(SST)anomalies and wind stress(τ)anomalies can be constructed by statistical methods,such as singular value decomposition(SVD).In recent years,the applications of artificial intelligence(AI)to climate modeling have shown promising prospects,and the integrations of AI-based models with dynamical models are active areas of research.This study constructs U-Net models for representing the relationship between SSTAs andτanomalies in the tropical Pacific;the UNet-derivedτmodel,denoted asτUNet,is then used to replace the original SVD-basedτmodel of an intermediate coupled model(ICM),forming a newly AI-integrated ICM,referred to as ICM-UNet.The simulation results obtained from ICM-UNet demonstrate their ability to represent the spatiotemporal variability of oceanic and atmospheric anomaly fields in the equatorial Pacific.In the ocean-only case study,theτUNet-derived wind stress anomaly fields are used to force the ocean component of the ICM,the results of which also indicate reasonable simulations of typical ENSO events.These results demonstrate the feasibility of integrating an AI-derived model with a physics-based dynamical model for ENSO modeling studies.Furthermore,the successful integration of the dynamical ocean models with the AI-based atmospheric wind model provides a novel approach to ocean-atmosphere interaction modeling studies.
基金supported by the Industry-Academia Collaboration Project of the Ministry of Education:A Study on the Blended Teaching Model of Chinese-English Translation in the Era of Artificial Intelligence(Project Fund No.231001363084506).
文摘This article explores in-class practice of blended teaching of Chinese-English(C-E)translation for English as a Foreign Language(EFL)majors in the era of artificial intelligence(AI).It examines the opportunities and challenges AI presents in enhancing translation education,particularly in fostering student engagement,improving teaching efficiency,and promoting self-motivated learning.Case study suggests that AI can enhance the flexibility of teaching and motivate students,yet challenges such as over-reliance on AI and diminished critical thinking need to be addressed.While acknowledging the indispensability of human translators,the article concludes that effective blended teaching requires purposeful curriculum design,proper integration of AI,and a collaborative effort of teachers and students to maximize the potential of AI while ensuring high-quality,independent learning outcomes.
基金the Agritech National Research Center and received funding from the European Union Next-Generation EU(PIANO NAZIONALE DI RIPRESA E RESILIENZA(PNRR)—MISSIONE 4 COMPONENTE 2,INVESTIMENTO 1.4—D.D.103217/06/2022,CN00000022).
文摘Artificial intelligence(AI)has transformed agricultural genetics,especially in the context of crop improvement strategies.Traditional breeding faces challenges such as polyploidy,high level of genomic heterogeneity,and complex gene-trait associations.By combining multi-omics data researchers learn more about the genetic and molecular basis of important agricultural traits.However,statistical methods are often insufficient to address the data complexity.By contrast,AI techniques,such as machine learning(ML)and deep learning(DL),are emerging as powerful tools to explore complexity.Algorithms such as random forests(RF)and support vector machines(SVM)can support genomic selection(GS)and trait value prediction.Furthermore,DL models such as convolutional neural networks(CNN)and long short-term memory networks(LSTM)dominate high-throughput phenotyping and time series analyses,providing accurate predictions for crop yield,disease resistance,and genotype adaptation.Large language models(LLMs)are able to integrate complex omics data.AI models can analyze large dataset,generated by genomics,transcriptomics,proteomics,metabolomics,and phenomic applications because algorithms can combine different inputs,such as DNA sequences,gene expression profiles,protein–protein interaction networks,metabolite concentrations,and phenotypic data under specific environmental conditions.The integration of individual models can improve prediction accuracy by reducing resource inputs and automating labor-intensive tasks involved in breeding programs.Some recent AI methods,such as gradient boosting machines(GBMs)and Transformer models,are increasingly being used to improve scalability and accuracy of predictive analytics.This review summarizes major advances in AI applications in agricultural genetics,highlighting the strengths and limitations of different ML and DL models and their role in integrating complex datasets.The study highlights the importance of artificial intelligence in understanding genomic complexity and promoting the development of innovative methods to improve crop performance.
文摘Satellite communications, pivotal for global connectivity, are increasingly converging with cutting-edge mobile networks, notably 5G, B5G, and 6G. This amalgamation heralds the promise of universal, high-velocity communication, yet it is not without its challenges. Paramount concerns encompass spectrum allocation, the harmonization of network architectures, and inherent latency issues in satellite transmissions. Potential mitigations, such as dynamic spectrum sharing and the deployment of edge computing, are explored as viable solutions. Looking ahead, the advent of quantum communications within satellite frameworks and the integration of AI spotlight promising research trajectories. These advancements aim to foster a seamless and synergistic coexistence between satellite communications and next-gen mobile networks.
基金funding sources,including the National Natural Science Foundation of China(No.51709254,No.32201384)Youth Innovation Promotion Association,Chinese Academy of Sciences(No.2020335)+1 种基金Key Research and Development Program of Hubei Province,China(2020BCA073)National Science&Technology Fundamental Resources Investigation Program of China(2019FY100602).
文摘The integration of artificial intelligence(AI)in aquaculture has been identified as a transformative force,enhancing various operational aspects from water quality management to genetic optimization.This review provides a comprehensive synthesis of recent advancements in AI applications within the aquaculture sector,underscoring the significant enhancements in production efficiency and environmental sustainability.Key AI-driven improvements,such as predictive analytics for disease management and optimized feeding protocols,are highlighted,demonstrating their contributions to reducing waste and improving biomass outputs.However,challenges remain in terms of data quality,system integration,and the socio-economic impacts of technological adoption across diverse aquacultural environments.This review also addresses the gaps in current research,particularly the lack of robust,scalable AI models and frameworks that can be universally applied.Future directions are discussed,emphasizing the need for interdisciplinary research and development to fully leverage AI potential in aquaculture.This study not only maps the current landscape of AI applications but also serves as a call for continued innovation and strategic collaborations to overcome existing barriers and realize the full benefits of AI in aquaculture.
文摘The intersection of artificial intelligence(AI)and software engineering marks a transformative phase in the technology industry.This paper delves into AI-driven software engineering,exploring its methodologies,implications,challenges,and benefits.Drawing from data sources such as GitHub and Bitbucket and insights from industry experts,the study offers a comprehensive view of the current landscape.While the results indicate a promising uptrend in the integration of AI techniques in software development,challenges like model interpretability,ethical concerns,and integration complexities emerge as significant.Nevertheless,the transformative potential of AI within software engineering is profound,ushering in new paradigms of efficiency,innovation,and user experience.The study concludes by emphasizing the need for further research,better tooling,ethical guidelines,and education to fully harness the potential of AI-driven software engineering.
文摘Embodied intelligence emphasizes the synergy between body,mind,and environment,offering a powerful framework for building more adaptive and interactive intelligent systems.Over the past decade,rapid advancements in artificial intelligence have driven remarkable achievements in perception,planning,and control tasks,particularly through the rise of deep learning.However,embodied intelligence–the integration of AI with a physical body interacting in real environments–remains a relatively underexplored frontier.Unlike disembodied systems that rely solely on static datasets,embodied agents learn through real-time interaction with their surroundings,leveraging perception-action loops to adaptively understand and manipulate the world.Inspired by human cognition,embodied intelligence emphasizes learning by doing,thereby offering the potential to generalize knowledge across tasks,environments,and sensorimotor experiences.
