The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unatt...The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics.展开更多
Dermatological diagnosis is inherently visual,greatly relying on clinicians′interpretation of images and accumulated experience.Traditional teaching models have long been constrained by limited case diversity,lack of...Dermatological diagnosis is inherently visual,greatly relying on clinicians′interpretation of images and accumulated experience.Traditional teaching models have long been constrained by limited case diversity,lack of personalization,and inadequate assessment of competency development.Recent advances in artificial intelligence(AI)offer new technological support for dermatology education.To address the risk of fragmented adoption,a process-oriented approach,conceptualizing AI-assisted dermatology education as an integrated system embedded throughout the learning process is adopted.Within this framework,AI is not only examined as an isolated tool but also as a component aligned with educational workflows.AI′s primary applications in dermatology education are analyzed,focusing on its potential to improve standardization so as to expand access to high-quality resources and support competency-based teaching,and facilitate lifelong learning.Meaningful educational benefits emerge when AI is systematically integrated into structured teaching processes.However,associated risks-including data bias,learner overreliance,implementation constraints,and potential impacts on medical humanities education must also be considered.Based on these findings,the strategic principles centered on educational objectives are proposed,emphasizing human-AI collaboration,transparency,and continuous governance to support the sustainable development of dermatology talent.展开更多
A methodology for the reduction of radar cross section(RCS)of cambered platforms within the target airspace is presented,which utilizes a dual-polarized ultra-wide-angle artificial electromagnetic absorbing surface.By...A methodology for the reduction of radar cross section(RCS)of cambered platforms within the target airspace is presented,which utilizes a dual-polarized ultra-wide-angle artificial electromagnetic absorbing surface.By applying the theory of generalized Brewster complex wave impedance matching,five distinct unit cell designs are developed to attain more than95%absorption rate for dual-polarized incident waves within five angular ranges:0°-30°,30°-50°,50°-60°,60°-70°,and 70°-80°.To optimally reduce the RCS of a cambered platform,the five types of units can be evenly distributed on the surface based on the local incident angles of plane waves originating from the target airspace.As an illustrative example,the leading edge of an airfoil is taken into account,and experimental measurements validate the efficiency of the proposed structure.Specifically,the absorbing surface achieves more than 10 dB of RCS reduction in the frequency ranges from 5-10 GHz(about66.7%relative bandwidth)for dual polarizations.展开更多
Sustainable aluminum alloys,renowned for their lower energy consumption and carbon emissions,present a critical path towards a circular materials economy.However,their design is fraught with challenges,including compl...Sustainable aluminum alloys,renowned for their lower energy consumption and carbon emissions,present a critical path towards a circular materials economy.However,their design is fraught with challenges,including complex performance variability due to impurity elements and the time-consuming,cost-prohibitive nature of traditional trial-and-error methods.The high-dimensional parameter space in processing optimization and the reliance on human expertise for quality control further complicate their development.This paper provides a comprehensive review of Artificial Intelligence(AI)techniques applied to sustainable aluminum alloy design,analyzing their methodologies and identifying key challenges and optimization strategies.We review how AI methods such as knowledge graphs,evolutionary algorithms,and machine learning transformconventional processes into efficient,data-driven workflows,thereby enhancing development speed and precision.The review explicitly highlights existing bottlenecks,including insufficient data quality and standardization,the complexity of cross-scale modeling,and the need for industrial coordination.We conclude that AI holds immense potential to drive the recycled aluminum industry toward a more sustainable and intelligent future.Future research is poised to leverage generative AI,autonomous experimental platforms,and blockchain for improved life-cycle management,while also focusing on developing physics-informed models and establishing standardized data ecosystems.展开更多
Background:High-mobility group box 1(HMGB1)is a critical damage-associated molecular pattern protein that participates in diverse physiological and pathological processes.However,its relevance to the prognosis of arti...Background:High-mobility group box 1(HMGB1)is a critical damage-associated molecular pattern protein that participates in diverse physiological and pathological processes.However,its relevance to the prognosis of artificial liver support therapy in patients with acute liver injury(ALF)remains unclear.Methods:Bioinformatics analyses were performed to identify HMGB1-interacting proteins and associated inflammatory signaling pathways.Peripheral blood samples were collected from ALF patients before and after artificial liver support therapy,and serum HMGB1 concentrations were quantified using ELISA.Primary mouse hepatocytes were stimulated with lipopolysaccharide(LPS)in vitro and HMGB1 expression was verified by western blot.Results:Single-cell transcriptomic profiling showed that HMGB1 is widely expressed across tissues and predominantly localized in the nucleus.In the liver,HMGB1 was primarily expressed in hepatocytes and hepatic stellate cells.STRING database analysis revealed that human HMGB1 interacts with multiple proteins,including TLR4,TP53,and BECN1.The constructed interaction network comprised 11 nodes with an average local clustering coefficient of 0.888,and the protein–protein interaction enrichment P-value was 1.42×10^(-5),indicating significant enrichment.Gene Ontology and KEGG pathway enrichment analyses demonstrated that HMGB1 is closely linked to inflammatory and injury-related signaling pathways,including the TLR and NLR pathways.Metabolomic profiling revealed significant metabolic alterations between patients with ALF and healthy controls under both positive and negative ion modes and functional analysis showed necroptosis was activated.The cell viability gradually decreased with time and dose under LPS treatment and extracellular HMGB1 was upregulated in LPS induced ALF model and patients(P<0.05).Serum HMGB1/RIPK3/MLKL levels were markedly elevated in ALF patients compared with controls(P<0.05)and progressively declined following artificial liver support therapy.Furthermore,elevated HMGB1 concentrations were positively correlated with unfavorable clinical outcomes.Conclusion:Peripheral blood HMGB1 levels are significantly increased in patients with acute liver failure,decrease following artificial liver support therapy,and are positively associated with poor clinical prognosis.展开更多
Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric ana...Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric analysis was conducted in accordance with the PRISMA 2020 statement.Peer-reviewed articles published in English from 2015 to 2025 were retrieved from Scopus,Web of Science,and PubMed.Thematic clustering was conducted using the Louvain algorithm and cosine similarity.A subset of 66 frequently cited articles was then qualitatively synthesized to capture core competencies across clusters.Results A total of 83,807 articles were included for bibliometric analysis.Of these,66 articles were chosen for thematic analysis.Five major thematic clusters were identified:remote care in primary settings,oncology and palliative care,nurse education and training,safety and quality in nursing practice,and geriatric and dementia care.Additionally,four competency domains were identified:telehealth and remote communication,health systems and informatics,digital tools in practice,and AI-powered decision support.A clear shift in research focus was observed,with the emphasis transitioning from foundational digital skills before the COVID-19 pandemic to more advanced competencies during the post-pandemic digital transformation,encompassing ethical reasoning,immersive technology use,and AI integration.Conclusions Integrating digital and AI technologies is reshaping nursing practice across various thematic areas and competency domains,highlighting a transition from foundational digital tasks to AI-supported decision-making and ethically informed technology use.This study provides a structured overview of evolving competencies in digital nursing and synthesizes evidence to support future research,curriculum design,and policy planning.展开更多
The rapid advancement of Artificial Intelligence(AI)has transformed educational practices,yet its integration with experiential pedagogies such as drama remains underexplored in English Language Teaching(ELT),particul...The rapid advancement of Artificial Intelligence(AI)has transformed educational practices,yet its integration with experiential pedagogies such as drama remains underexplored in English Language Teaching(ELT),particularly in pre-service teacher education.This study examines how AI-supported drama pedagogy contributes to the professional development of pre-service English teachers,focusing on reflective practice,pedagogical adaptability,creativity,intercultural awareness,and sustainability-oriented teaching perspectives.Grounded in sociocultural theory,experiential learning,and Education for Sustainable Development(ESD),the research adopts an interpretive qualitative case study design conducted over a 12-week elective course titled“Drama in ELT”at a foundation university in Istanbul,Türkiye.Participants included 40 second-year pre-service teachers,with 15 volunteers taking part in semi-structured focus group interviews.Data were collected through open-ended questionnaires and focus groups and analyzed using reflexive thematic analysis.Four interrelated themes emerged:creativity and pedagogical innovation,intercultural awareness and empathy,problem-solving and adaptability,and reflective professional growth with ethical awareness.Findings suggest that AI acted as a mediational scaffold that enriched drama-based learning while preserving human agency.The study concludes that integrating AI with drama offers a meaningful model for sustainable teacher education aligned with SDG 4(Quality Education)and SDG 9(Industry,Innovation and Infrastructure).展开更多
erized by a periodic real-space modulation of the superconducting pairing order parameter,is a novel quantum phase observed in superconducting(SC)systems.It is believed to play a key role in understanding the pseudoga...erized by a periodic real-space modulation of the superconducting pairing order parameter,is a novel quantum phase observed in superconducting(SC)systems.It is believed to play a key role in understanding the pseudogap phase of superconductors and has recently been discovered in bulk cuprates,transition-metal dichalcogenide,and other unconventional superconductors.However,artificially engineered PDW in designable two-dimensional materials remain rare.In this paper,we report a strain-assisted strategy to realize cooper-pair density modulation in a van der Waals heterostructure:graphene on SC 2H-NbSe2.Superconductivity is induced in graphene via the proximity effect.Meanwhile,the graphene membrane spontaneously buckles into a periodic structure owing to strain,featuring a spatially modulated local density of states(LDOS).The interplay between the spatially modulated LDOS and the proximity-induced superconductivity results in an oscillatory pair density determined by the buckled geometry,constituting an artificial PDW.This approach enables the engineering of PDWs with periodicities of up to tens of nanometers and allows their realization in a variety of heterostructures with tailored designs.Our work provides new insights into the investigation of PDW physics using predesigned two-dimensional materials.展开更多
文摘The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics.
文摘Dermatological diagnosis is inherently visual,greatly relying on clinicians′interpretation of images and accumulated experience.Traditional teaching models have long been constrained by limited case diversity,lack of personalization,and inadequate assessment of competency development.Recent advances in artificial intelligence(AI)offer new technological support for dermatology education.To address the risk of fragmented adoption,a process-oriented approach,conceptualizing AI-assisted dermatology education as an integrated system embedded throughout the learning process is adopted.Within this framework,AI is not only examined as an isolated tool but also as a component aligned with educational workflows.AI′s primary applications in dermatology education are analyzed,focusing on its potential to improve standardization so as to expand access to high-quality resources and support competency-based teaching,and facilitate lifelong learning.Meaningful educational benefits emerge when AI is systematically integrated into structured teaching processes.However,associated risks-including data bias,learner overreliance,implementation constraints,and potential impacts on medical humanities education must also be considered.Based on these findings,the strategic principles centered on educational objectives are proposed,emphasizing human-AI collaboration,transparency,and continuous governance to support the sustainable development of dermatology talent.
基金supported by the National Key Research and Development Program of China(2023YFB3907304-3)the National Natural Science Foundation of China(NSFC)(62271050)。
文摘A methodology for the reduction of radar cross section(RCS)of cambered platforms within the target airspace is presented,which utilizes a dual-polarized ultra-wide-angle artificial electromagnetic absorbing surface.By applying the theory of generalized Brewster complex wave impedance matching,five distinct unit cell designs are developed to attain more than95%absorption rate for dual-polarized incident waves within five angular ranges:0°-30°,30°-50°,50°-60°,60°-70°,and 70°-80°.To optimally reduce the RCS of a cambered platform,the five types of units can be evenly distributed on the surface based on the local incident angles of plane waves originating from the target airspace.As an illustrative example,the leading edge of an airfoil is taken into account,and experimental measurements validate the efficiency of the proposed structure.Specifically,the absorbing surface achieves more than 10 dB of RCS reduction in the frequency ranges from 5-10 GHz(about66.7%relative bandwidth)for dual polarizations.
基金the financial support of Shanghai Natural Science Foundation(25ZR1401430)Science and Technology Cooperation Program of Shanghai Jiao Tong University in Inner Mongolia Autonomous Region-Action Plan of Shanghai Jiao Tong University for“Revitalizing Inner Mongolia through Science and Technology”(2023XYJG0001-01-01).
文摘Sustainable aluminum alloys,renowned for their lower energy consumption and carbon emissions,present a critical path towards a circular materials economy.However,their design is fraught with challenges,including complex performance variability due to impurity elements and the time-consuming,cost-prohibitive nature of traditional trial-and-error methods.The high-dimensional parameter space in processing optimization and the reliance on human expertise for quality control further complicate their development.This paper provides a comprehensive review of Artificial Intelligence(AI)techniques applied to sustainable aluminum alloy design,analyzing their methodologies and identifying key challenges and optimization strategies.We review how AI methods such as knowledge graphs,evolutionary algorithms,and machine learning transformconventional processes into efficient,data-driven workflows,thereby enhancing development speed and precision.The review explicitly highlights existing bottlenecks,including insufficient data quality and standardization,the complexity of cross-scale modeling,and the need for industrial coordination.We conclude that AI holds immense potential to drive the recycled aluminum industry toward a more sustainable and intelligent future.Future research is poised to leverage generative AI,autonomous experimental platforms,and blockchain for improved life-cycle management,while also focusing on developing physics-informed models and establishing standardized data ecosystems.
基金supported by the National Natural Science Foundation of China(No.82360120)the Kunming Medical University Joint Special Project on Applied Basic Research(202401AY070001-134),and project iGandanF-1082022-RGG049+2 种基金the Open Project of Yunnan Provincial Clinical Medical Center for Digestive System Diseases(2022LCZXXF-XH07/17)the 14th Undergraduate Scientific Research Project of Mudanjiang Medical University(2024057)Yunnan Provincial Key Laboratory of Clinical Virology(No.2023A4010403-04).
文摘Background:High-mobility group box 1(HMGB1)is a critical damage-associated molecular pattern protein that participates in diverse physiological and pathological processes.However,its relevance to the prognosis of artificial liver support therapy in patients with acute liver injury(ALF)remains unclear.Methods:Bioinformatics analyses were performed to identify HMGB1-interacting proteins and associated inflammatory signaling pathways.Peripheral blood samples were collected from ALF patients before and after artificial liver support therapy,and serum HMGB1 concentrations were quantified using ELISA.Primary mouse hepatocytes were stimulated with lipopolysaccharide(LPS)in vitro and HMGB1 expression was verified by western blot.Results:Single-cell transcriptomic profiling showed that HMGB1 is widely expressed across tissues and predominantly localized in the nucleus.In the liver,HMGB1 was primarily expressed in hepatocytes and hepatic stellate cells.STRING database analysis revealed that human HMGB1 interacts with multiple proteins,including TLR4,TP53,and BECN1.The constructed interaction network comprised 11 nodes with an average local clustering coefficient of 0.888,and the protein–protein interaction enrichment P-value was 1.42×10^(-5),indicating significant enrichment.Gene Ontology and KEGG pathway enrichment analyses demonstrated that HMGB1 is closely linked to inflammatory and injury-related signaling pathways,including the TLR and NLR pathways.Metabolomic profiling revealed significant metabolic alterations between patients with ALF and healthy controls under both positive and negative ion modes and functional analysis showed necroptosis was activated.The cell viability gradually decreased with time and dose under LPS treatment and extracellular HMGB1 was upregulated in LPS induced ALF model and patients(P<0.05).Serum HMGB1/RIPK3/MLKL levels were markedly elevated in ALF patients compared with controls(P<0.05)and progressively declined following artificial liver support therapy.Furthermore,elevated HMGB1 concentrations were positively correlated with unfavorable clinical outcomes.Conclusion:Peripheral blood HMGB1 levels are significantly increased in patients with acute liver failure,decrease following artificial liver support therapy,and are positively associated with poor clinical prognosis.
基金supported by grants for development of new faculty staff,Ratchadaphiseksomphot Fund,Chulalongkorn University,Thailand.
文摘Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric analysis was conducted in accordance with the PRISMA 2020 statement.Peer-reviewed articles published in English from 2015 to 2025 were retrieved from Scopus,Web of Science,and PubMed.Thematic clustering was conducted using the Louvain algorithm and cosine similarity.A subset of 66 frequently cited articles was then qualitatively synthesized to capture core competencies across clusters.Results A total of 83,807 articles were included for bibliometric analysis.Of these,66 articles were chosen for thematic analysis.Five major thematic clusters were identified:remote care in primary settings,oncology and palliative care,nurse education and training,safety and quality in nursing practice,and geriatric and dementia care.Additionally,four competency domains were identified:telehealth and remote communication,health systems and informatics,digital tools in practice,and AI-powered decision support.A clear shift in research focus was observed,with the emphasis transitioning from foundational digital skills before the COVID-19 pandemic to more advanced competencies during the post-pandemic digital transformation,encompassing ethical reasoning,immersive technology use,and AI integration.Conclusions Integrating digital and AI technologies is reshaping nursing practice across various thematic areas and competency domains,highlighting a transition from foundational digital tasks to AI-supported decision-making and ethically informed technology use.This study provides a structured overview of evolving competencies in digital nursing and synthesizes evidence to support future research,curriculum design,and policy planning.
文摘The rapid advancement of Artificial Intelligence(AI)has transformed educational practices,yet its integration with experiential pedagogies such as drama remains underexplored in English Language Teaching(ELT),particularly in pre-service teacher education.This study examines how AI-supported drama pedagogy contributes to the professional development of pre-service English teachers,focusing on reflective practice,pedagogical adaptability,creativity,intercultural awareness,and sustainability-oriented teaching perspectives.Grounded in sociocultural theory,experiential learning,and Education for Sustainable Development(ESD),the research adopts an interpretive qualitative case study design conducted over a 12-week elective course titled“Drama in ELT”at a foundation university in Istanbul,Türkiye.Participants included 40 second-year pre-service teachers,with 15 volunteers taking part in semi-structured focus group interviews.Data were collected through open-ended questionnaires and focus groups and analyzed using reflexive thematic analysis.Four interrelated themes emerged:creativity and pedagogical innovation,intercultural awareness and empathy,problem-solving and adaptability,and reflective professional growth with ethical awareness.Findings suggest that AI acted as a mediational scaffold that enriched drama-based learning while preserving human agency.The study concludes that integrating AI with drama offers a meaningful model for sustainable teacher education aligned with SDG 4(Quality Education)and SDG 9(Industry,Innovation and Infrastructure).
基金supported by the National Natural Science Foundation of China (Grant Nos.12474477,12550405,and 61888102)the Beijing Outstanding Young Scientist Program+4 种基金the National Key R&D Program of China (Grant No.2024YFA1207700)the Fundamental Research Funds for the Central Universitiesthe Scientific Research Innovation Capability Support Project for Young Faculty (Grant No.SRICSPYF- ZY2025071)the Robotic AI-Scientist Platform of the Chinese Academy of Sciencesfinancial support from the Flemish Research Foundation (Grant Nos.FWO/11E5821N and FWO/G0A5921N)。
文摘erized by a periodic real-space modulation of the superconducting pairing order parameter,is a novel quantum phase observed in superconducting(SC)systems.It is believed to play a key role in understanding the pseudogap phase of superconductors and has recently been discovered in bulk cuprates,transition-metal dichalcogenide,and other unconventional superconductors.However,artificially engineered PDW in designable two-dimensional materials remain rare.In this paper,we report a strain-assisted strategy to realize cooper-pair density modulation in a van der Waals heterostructure:graphene on SC 2H-NbSe2.Superconductivity is induced in graphene via the proximity effect.Meanwhile,the graphene membrane spontaneously buckles into a periodic structure owing to strain,featuring a spatially modulated local density of states(LDOS).The interplay between the spatially modulated LDOS and the proximity-induced superconductivity results in an oscillatory pair density determined by the buckled geometry,constituting an artificial PDW.This approach enables the engineering of PDWs with periodicities of up to tens of nanometers and allows their realization in a variety of heterostructures with tailored designs.Our work provides new insights into the investigation of PDW physics using predesigned two-dimensional materials.
