Many corporations are complaining that they cannot make decisions correctly. This article describes virtual laboratory (VL), an organization which can help managers make decision. The virtual laboratory based on Intra...Many corporations are complaining that they cannot make decisions correctly. This article describes virtual laboratory (VL), an organization which can help managers make decision. The virtual laboratory based on Intranet uses many methods to draw policies for the corporation by analyzing business processes. It is a tool for learning organization too.展开更多
Digital twins(DTs)are rapidly emerging as transformative tools in materials science and engineering,enabling real-time data integration,predictive modeling,and virtual testing.This study presents a systematic bibliome...Digital twins(DTs)are rapidly emerging as transformative tools in materials science and engineering,enabling real-time data integration,predictive modeling,and virtual testing.This study presents a systematic bibliometric review of 1106 peer-reviewed articles published in the last decade in Scopus and Web of Science.Using a five-stage methodology,the review examines publication trends,thematic areas,citation metrics,and keyword patterns.The results reveal exponential growth in scientific output,with Materials Theory,Computation,and Data Science as the most represented area.A thematic analysis of the most cited documents identifies four major research streams:foundational frameworks,DTs in additive manufacturing,sector-specific applications,and intelligent production systems.Keyword co-occurrence and strategic mapping show a strong foundation in modeling,simulation,and optimization,with growing links to machine learning and sustainability.The review highlights current challenges and proposes future research directions for advancing DTs in materials science.展开更多
Artificial intelligence(AI)technology is increasingly used in the field of education,but its application in molecular biology experimental teaching still faces challenges.In order to explore the application prospects ...Artificial intelligence(AI)technology is increasingly used in the field of education,but its application in molecular biology experimental teaching still faces challenges.In order to explore the application prospects of AI technology in molecular biology experimental teaching,this paper discusses the application of AI technology in molecular biology experimental teaching,focusing on the construction and application of virtual laboratories.At the same time,the advantages,challenges and future development directions of AI technology application are analyzed.The study found that AI technology has broad application prospects in molecular biology experimental teaching.AI technology can overcome many limitations in traditional experimental teaching,and can also provide personalized learning experience,real-time feedback and evaluation,and simulate complex molecular processes.However,the application of AI technology also faces challenges such as technology cost,teacher training,and curriculum design.In summary,the application of AI technology in molecular biology experimental teaching has significant advantages and can effectively improve teaching quality and learning effects.In the future,we should strengthen the integration of AI technology and traditional teaching methods,develop more AI teaching tools suitable for the characteristics of molecular biology,and focus on cultivating students’practical ability and innovative thinking.This study provides new ideas and directions for promoting the reform and innovation of molecular biology experimental teaching.展开更多
Generative Artificial Intelligence(GAI)refers to a class of AI systems capable of creating novel,coherent,and contextually relevant content—such as text,images,audio,and video—based on patterns learned from extensiv...Generative Artificial Intelligence(GAI)refers to a class of AI systems capable of creating novel,coherent,and contextually relevant content—such as text,images,audio,and video—based on patterns learned from extensive training datasets.The public release and rapid refinement of large language models(LLMs)like ChatGPT have accelerated the adoption of GAI across various medical specialties,offering new tools for education,clinical simulation,and research.Dermatology training,which heavily relies on visual pattern recognition and requires extensive exposure to diverse morphological presentations,faces persistent challenges such as uneven distribu-tion of educational resources,limited patient exposure for rare conditions,and variability in teaching quality.Exploring the integration of GAI into pedagogical frameworks offers innovative approaches to address these challenges,potentially enhancing the quality,standardization,scalability,and accessibility of dermatology ed-ucation.This comprehensive review examines the core concepts and technical foundations of GAI,highlights its specific applications within dermatology teaching and learning—including simulated case generation,per-sonalized learning pathways,and academic support—and discusses the current limitations,practical challenges,and ethical considerations surrounding its use.The aim is to provide a balanced perspective on the significant potential of GAI for transforming dermatology education and to offer evidence-based insights to guide future exploration,implementation,and policy development.展开更多
This paper focuses on the presence of nodules of insoluble materials within salt specimens,and their effect on the volumetric strain measurements and the dilatancy phenomenon.We analyzed experimental results of over 1...This paper focuses on the presence of nodules of insoluble materials within salt specimens,and their effect on the volumetric strain measurements and the dilatancy phenomenon.We analyzed experimental results of over 120 conventional triaxial compression tests,and found that in 20%of the cases,the volumetric strain measurements were atypical.We also noted that the natural variability of the specimens can lead to a non-negligible data scattering in the volumetric strain measurements when different specimens are subjected to the same test.This is expected given the small magnitude of those strains,but it occasionally implies that the corresponding specimens are not representative of the volumetric behavior of the studied rock.In order to understand these results,we numerically investigated salt specimens modeled as halite matrices with inclusions of impurities.Simulations of triaxial compression tests on these structures proved that such heterogeneities can induce dilatancy,and their presence can lead to the appearance of tensile zones which is physically translated into a micro-cracking activity.The modeling approach is validated as the patterns displayed in the numerical results are identical to that in the laboratory.It was then employed to explain the observed irregularities in experimental results.We studied the natural variability effect as well and proposed a methodology to overcome the issue of specimen representativity from both deviatoric and volumetric perspectives.展开更多
文摘Many corporations are complaining that they cannot make decisions correctly. This article describes virtual laboratory (VL), an organization which can help managers make decision. The virtual laboratory based on Intranet uses many methods to draw policies for the corporation by analyzing business processes. It is a tool for learning organization too.
文摘Digital twins(DTs)are rapidly emerging as transformative tools in materials science and engineering,enabling real-time data integration,predictive modeling,and virtual testing.This study presents a systematic bibliometric review of 1106 peer-reviewed articles published in the last decade in Scopus and Web of Science.Using a five-stage methodology,the review examines publication trends,thematic areas,citation metrics,and keyword patterns.The results reveal exponential growth in scientific output,with Materials Theory,Computation,and Data Science as the most represented area.A thematic analysis of the most cited documents identifies four major research streams:foundational frameworks,DTs in additive manufacturing,sector-specific applications,and intelligent production systems.Keyword co-occurrence and strategic mapping show a strong foundation in modeling,simulation,and optimization,with growing links to machine learning and sustainability.The review highlights current challenges and proposes future research directions for advancing DTs in materials science.
基金Construction Project of Teaching Quality and Teaching Reform Engineering for Undergraduate Universities in Guangdong Province(Project No.:2023-248)First-Class Undergraduate Course in Guangdong Province-Molecular Biology Experiment 2023+1 种基金Curriculum Ideology and Politics Demonstration Project of Lingnan Normal University(Project No.:2022-6&2024-44)Construction Project of Teaching Quality and Teaching Reform Engineering of Lingnan Normal University(Project No.:2024-24)。
文摘Artificial intelligence(AI)technology is increasingly used in the field of education,but its application in molecular biology experimental teaching still faces challenges.In order to explore the application prospects of AI technology in molecular biology experimental teaching,this paper discusses the application of AI technology in molecular biology experimental teaching,focusing on the construction and application of virtual laboratories.At the same time,the advantages,challenges and future development directions of AI technology application are analyzed.The study found that AI technology has broad application prospects in molecular biology experimental teaching.AI technology can overcome many limitations in traditional experimental teaching,and can also provide personalized learning experience,real-time feedback and evaluation,and simulate complex molecular processes.However,the application of AI technology also faces challenges such as technology cost,teacher training,and curriculum design.In summary,the application of AI technology in molecular biology experimental teaching has significant advantages and can effectively improve teaching quality and learning effects.In the future,we should strengthen the integration of AI technology and traditional teaching methods,develop more AI teaching tools suitable for the characteristics of molecular biology,and focus on cultivating students’practical ability and innovative thinking.This study provides new ideas and directions for promoting the reform and innovation of molecular biology experimental teaching.
文摘Generative Artificial Intelligence(GAI)refers to a class of AI systems capable of creating novel,coherent,and contextually relevant content—such as text,images,audio,and video—based on patterns learned from extensive training datasets.The public release and rapid refinement of large language models(LLMs)like ChatGPT have accelerated the adoption of GAI across various medical specialties,offering new tools for education,clinical simulation,and research.Dermatology training,which heavily relies on visual pattern recognition and requires extensive exposure to diverse morphological presentations,faces persistent challenges such as uneven distribu-tion of educational resources,limited patient exposure for rare conditions,and variability in teaching quality.Exploring the integration of GAI into pedagogical frameworks offers innovative approaches to address these challenges,potentially enhancing the quality,standardization,scalability,and accessibility of dermatology ed-ucation.This comprehensive review examines the core concepts and technical foundations of GAI,highlights its specific applications within dermatology teaching and learning—including simulated case generation,per-sonalized learning pathways,and academic support—and discusses the current limitations,practical challenges,and ethical considerations surrounding its use.The aim is to provide a balanced perspective on the significant potential of GAI for transforming dermatology education and to offer evidence-based insights to guide future exploration,implementation,and policy development.
文摘This paper focuses on the presence of nodules of insoluble materials within salt specimens,and their effect on the volumetric strain measurements and the dilatancy phenomenon.We analyzed experimental results of over 120 conventional triaxial compression tests,and found that in 20%of the cases,the volumetric strain measurements were atypical.We also noted that the natural variability of the specimens can lead to a non-negligible data scattering in the volumetric strain measurements when different specimens are subjected to the same test.This is expected given the small magnitude of those strains,but it occasionally implies that the corresponding specimens are not representative of the volumetric behavior of the studied rock.In order to understand these results,we numerically investigated salt specimens modeled as halite matrices with inclusions of impurities.Simulations of triaxial compression tests on these structures proved that such heterogeneities can induce dilatancy,and their presence can lead to the appearance of tensile zones which is physically translated into a micro-cracking activity.The modeling approach is validated as the patterns displayed in the numerical results are identical to that in the laboratory.It was then employed to explain the observed irregularities in experimental results.We studied the natural variability effect as well and proposed a methodology to overcome the issue of specimen representativity from both deviatoric and volumetric perspectives.
