Since Prof. Jorg SCHLAICH from the University of Stuttgart in Germany built the first solar chimney power plant （SCPP） prototype in the early 1980s, research on SCPP technology has aroused worldwide attention from e...Since Prof. Jorg SCHLAICH from the University of Stuttgart in Germany built the first solar chimney power plant （SCPP） prototype in the early 1980s, research on SCPP technology has aroused worldwide attention from experiment, to theory and then feasibility studies for large-scale commercial application.展开更多
Different synthetic aperture radar(SAR)sensors vary significantly in resolution,polarization modes,and frequency bands,making it difficult to directly apply existing models to newly launched SAR satellites.These new s...Different synthetic aperture radar(SAR)sensors vary significantly in resolution,polarization modes,and frequency bands,making it difficult to directly apply existing models to newly launched SAR satellites.These new systems require large amounts of labeled data for model retraining,but collecting sufficient data in a short time is often infeasible.To address this contradiction,this paper proposes a data generation and transfer framework,integrating a stable diffusion model with attention distillation,that leverages historical SAR data to synthesize training data tailored to the unique characteristics of new SAR systems.Specifically,we fine-tune the low-rank adaptation(LoRA)modules within the multimodal diffusion transformer(MM-DiT)architecture to enable class-controllable SAR image generation guided by textual prompts.To ensure that the generated images reflect the statistical properties and imaging characteristics of the target SAR system,we further introduce an attention distillation mechanism that transfers sensor-specific features,such as spatial texture,speckle distribution,and structural patterns,from real target-domain data to the generative model.Extensive experiments on multi-class aircraft target datasets from two real spaceborne SAR systems demonstrate the effectiveness of the proposed approach in alleviating data scarcity and supporting cross-sensor remote sensing applications.展开更多
Against the backdrop of growing health awareness and a younger consumer market,healthcare brands face the dualchallenge of revitalizing their visual identity while preserving brand heritage.This is particularly pressi...Against the backdrop of growing health awareness and a younger consumer market,healthcare brands face the dualchallenge of revitalizing their visual identity while preserving brand heritage.This is particularly pressing for traditional Chinesemedicine brands,which urgently need to adopt innovative design approaches to develop brand IP identities that combine culturaldepth with contemporary appeal.This study centers on Yunnan Baiyao as a core case and integrates generative ArtificialIntelligence(AI)technology to explore strategies for updating brand identities that align with brand tonality while offeringconsistency and visual recognizability.First,the study conducts a literature review and case analysis to examine the developmenttrends in healthcare brand identity design.It then performs a Strengths,Weakness,Opportunities and Threats(SWOT)analysisof Yunnan Baiyao’s existing visual assets to clarify the strengths and areas for improvement in its brand identity.Based on thebrand’s core values and cultural background,prompt engineering and image generation experiments using generative AI arecarried out to explore systematic expressions of IP characters,graphic styles,and communication media.The research aims toconstruct a brand identity system that blends tradition and modernity through innovative applications of visual language,colorschemes,and narrative strategies—ultimately enhancing brand recognizability,emotional resonance,and marketcompetitiveness.展开更多
Generative Artificial Intelligence technology embedded brings new opportunities and challenges for thehigh-quality development of ice and snow tourism industry.Using literature and other research methods,we constructe...Generative Artificial Intelligence technology embedded brings new opportunities and challenges for thehigh-quality development of ice and snow tourism industry.Using literature and other research methods,we constructed the element linkage and system of generative AI technology to drive the high-quality development of theice and snow tourism industry and proposed the digital governance path.The results showed that:(1)The factorlinkage of high-quality development of ice and snow tourism industry with integrated basic structure and coupledcentral system of multi-factor linkage;multi-directional strategic objectives and synergistic triple benefits;and intelligent operation logic and precise matching of supply and demand has been constructed.(2)Based on the synergyof digital governance elements,a five-factor synergistic development system was built with digital talent elements,technological innovation elements,product innovation elements,data resource elements and channel innovationelements.(3)The synergy of elements provided digital governance path guidelines for the high-quality developmentof ice and snow tourism industry,digital governance system:precise supply of policies and regulations,andstrengthening of soft and hard constraints to promote;digital governance structure:building a three-dimensionalregulatory mechanism,and advancing the governance process of targeting;digital governance subject:driving thetwo-way reach of the governance subject,and strengthening the common governance of pluralistic subjects;digitalgovernance capacity:precise allocation of governance resources,and driving the transformation and upgrading ofelemental endowments.The results of this research can help to provide ideas and reference for the high-qualitydevelopment of ice and snow tourism industry empowered by Generative Artificial Intelligence technology andimprove the theoretical research on the high-quality development of ice and snow tourism industry.展开更多
Humanity’s understanding of Earth has been a journey of continuous exploration and deepening insight.In response to the increasingly prominent global environmental issues arising from human activity,Anthropocene scie...Humanity’s understanding of Earth has been a journey of continuous exploration and deepening insight.In response to the increasingly prominent global environmental issues arising from human activity,Anthropocene science has emerged as an essential new frontier in Earth sciences,following the foundational theories of plate tectonics,global change,and Earth system science.1 In addition to this development,the rise of generative AI technology,exemplified by NVIDIA’s Earth-2 digital twin,heralds a new era of AI-driven scientific research.展开更多
文摘Since Prof. Jorg SCHLAICH from the University of Stuttgart in Germany built the first solar chimney power plant （SCPP） prototype in the early 1980s, research on SCPP technology has aroused worldwide attention from experiment, to theory and then feasibility studies for large-scale commercial application.
基金supported in part by the National Natural Science Foundations of China(Nos.62201027,62271034)。
文摘Different synthetic aperture radar(SAR)sensors vary significantly in resolution,polarization modes,and frequency bands,making it difficult to directly apply existing models to newly launched SAR satellites.These new systems require large amounts of labeled data for model retraining,but collecting sufficient data in a short time is often infeasible.To address this contradiction,this paper proposes a data generation and transfer framework,integrating a stable diffusion model with attention distillation,that leverages historical SAR data to synthesize training data tailored to the unique characteristics of new SAR systems.Specifically,we fine-tune the low-rank adaptation(LoRA)modules within the multimodal diffusion transformer(MM-DiT)architecture to enable class-controllable SAR image generation guided by textual prompts.To ensure that the generated images reflect the statistical properties and imaging characteristics of the target SAR system,we further introduce an attention distillation mechanism that transfers sensor-specific features,such as spatial texture,speckle distribution,and structural patterns,from real target-domain data to the generative model.Extensive experiments on multi-class aircraft target datasets from two real spaceborne SAR systems demonstrate the effectiveness of the proposed approach in alleviating data scarcity and supporting cross-sensor remote sensing applications.
文摘Against the backdrop of growing health awareness and a younger consumer market,healthcare brands face the dualchallenge of revitalizing their visual identity while preserving brand heritage.This is particularly pressing for traditional Chinesemedicine brands,which urgently need to adopt innovative design approaches to develop brand IP identities that combine culturaldepth with contemporary appeal.This study centers on Yunnan Baiyao as a core case and integrates generative ArtificialIntelligence(AI)technology to explore strategies for updating brand identities that align with brand tonality while offeringconsistency and visual recognizability.First,the study conducts a literature review and case analysis to examine the developmenttrends in healthcare brand identity design.It then performs a Strengths,Weakness,Opportunities and Threats(SWOT)analysisof Yunnan Baiyao’s existing visual assets to clarify the strengths and areas for improvement in its brand identity.Based on thebrand’s core values and cultural background,prompt engineering and image generation experiments using generative AI arecarried out to explore systematic expressions of IP characters,graphic styles,and communication media.The research aims toconstruct a brand identity system that blends tradition and modernity through innovative applications of visual language,colorschemes,and narrative strategies—ultimately enhancing brand recognizability,emotional resonance,and marketcompetitiveness.
基金The 2024 Humanities and Social Sciences Program for UniversitiesDepartment of Education of Guizhou Province(2024RW201)+3 种基金The Fujian Province Social Science Planning Grant in 2023(FJ2023B017)The Guizhou University Humanities and Social Sciences Research Project(2024RW199)The Research Program for Young and Middle-aged Teachers in Fujian Province in 2021(JAS21430)The General Project of the Ministry of Education’s Humanities and Social Sciences Fund in 2021(21YJC890008)。
文摘Generative Artificial Intelligence technology embedded brings new opportunities and challenges for thehigh-quality development of ice and snow tourism industry.Using literature and other research methods,we constructed the element linkage and system of generative AI technology to drive the high-quality development of theice and snow tourism industry and proposed the digital governance path.The results showed that:(1)The factorlinkage of high-quality development of ice and snow tourism industry with integrated basic structure and coupledcentral system of multi-factor linkage;multi-directional strategic objectives and synergistic triple benefits;and intelligent operation logic and precise matching of supply and demand has been constructed.(2)Based on the synergyof digital governance elements,a five-factor synergistic development system was built with digital talent elements,technological innovation elements,product innovation elements,data resource elements and channel innovationelements.(3)The synergy of elements provided digital governance path guidelines for the high-quality developmentof ice and snow tourism industry,digital governance system:precise supply of policies and regulations,andstrengthening of soft and hard constraints to promote;digital governance structure:building a three-dimensionalregulatory mechanism,and advancing the governance process of targeting;digital governance subject:driving thetwo-way reach of the governance subject,and strengthening the common governance of pluralistic subjects;digitalgovernance capacity:precise allocation of governance resources,and driving the transformation and upgrading ofelemental endowments.The results of this research can help to provide ideas and reference for the high-qualitydevelopment of ice and snow tourism industry empowered by Generative Artificial Intelligence technology andimprove the theoretical research on the high-quality development of ice and snow tourism industry.
基金supported by the National Natural Science Foundation of China(grant nos.41925007 and U21A2013).
文摘Humanity’s understanding of Earth has been a journey of continuous exploration and deepening insight.In response to the increasingly prominent global environmental issues arising from human activity,Anthropocene science has emerged as an essential new frontier in Earth sciences,following the foundational theories of plate tectonics,global change,and Earth system science.1 In addition to this development,the rise of generative AI technology,exemplified by NVIDIA’s Earth-2 digital twin,heralds a new era of AI-driven scientific research.
