Based on Conceptual Metaphor Theory(CMT),this paper creates a tiny corpus of ChatGPT-written speeches.Through employing a corpus-driven approach,this study analyzes the identification and utilization of conceptual met...Based on Conceptual Metaphor Theory(CMT),this paper creates a tiny corpus of ChatGPT-written speeches.Through employing a corpus-driven approach,this study analyzes the identification and utilization of conceptual metaphors in artificial intelligence(AI)languages.The AI demonstrated its capacity to utilize metaphors in the metaphoric corpora through the display of diversity,non-arbitrariness,repetition,and intersectionality in the selection of source domains.It often uses vocabulary combinations with clear similarities to establish metaphorical meaning.In the literal sense,the outcomes of metaphor identification by artificial intelligence differ significantly from those of humans.Therefore,there is a need to develop advanced automatic models for identifying metaphors in order to enhance the precision of metaphor identification consistently.展开更多
Rechargeable batteries are pivotal for achieving carbon neutrality and enabling the renewable energy transition.Their advancement requires inno-vations at micro(materials),device(manufacturing),and system(control and ...Rechargeable batteries are pivotal for achieving carbon neutrality and enabling the renewable energy transition.Their advancement requires inno-vations at micro(materials),device(manufacturing),and system(control and optimization)levels.However,traditional trial-and-error approaches are inadequate for modern scientific demands.As a transformative artificial intelligence(AI)technology,large language models(LLMs)deliver powerful semantic understanding and reasoning capabilities,driving a paradigm shift in battery research to address multilevel innovation needs.Neverthe-less,this field still faces dual challenges:ambiguous technical roadmaps and fragmented progress in stage-specific achievements.This review sys-tematically consolidates recent advances in applying LLMs to battery research,distilling core findings across four critical domains:knowledge integration,materials discovery,manufacturing processes,and system management.To address key bottlenecks—including limited model inter-pretability,inadequate alignment with electrochemical mechanisms,and real-world data adaptation challenges—we propose structured frameworks for deep integration of battery research and LLMs,alongside defined future technical pathways.These frameworks bridge fundamental battery science with AI-driven innovation paradigms to facilitate groundbreaking advances in next-generation battery technologies.展开更多
The expert system MUST (Mining Under Structures) shown in this paper and established by the authors is a preliminary expert system to solve the policy-making problems for mining under structures by means of computers ...The expert system MUST (Mining Under Structures) shown in this paper and established by the authors is a preliminary expert system to solve the policy-making problems for mining under structures by means of computers instead of humanbeing. Based on the experience of relative experts,the authors established a knowledge base about the minings under structures,researched into reasonable method to simulate thinking processes of human experts when they are solving the problems, established the network of an expert system and named it ' MUST system' . MUST system uses the method of the structural system analysis approach. A kind of methods of Turbo Prolog and Fortran 77 language alternations is designed to meet the needs of exchange information within the MUST system. Based on this kind of methods MUST system has been constructed and realised on IBM-PC computer. For verifying the correctness, suitability and reliablity of MUST system,some practical examples of minings under structures were tentatively solved using MUST system,whose results are satisfactory.展开更多
This paper introduces the creation of a little-known international auxiliary language by Giuseppe Peano,an Italian mathematician.The language,known as Latino sine flexione,later as Interlingua,was developed as a writt...This paper introduces the creation of a little-known international auxiliary language by Giuseppe Peano,an Italian mathematician.The language,known as Latino sine flexione,later as Interlingua,was developed as a written language of international communication primarily for use among mathematicians.Based on Latin,the language has a simplified grammatical structure,and its lexicon draws heavily from Latin and European languages.Peano's reflections on the similarities between Latino sine flexione,Chinese,English,and classical Latin are presented,along with some comparisons of translations of Chinese texts into these languages.The paper also examines scientific and mathematical texts to highlight the similarities between technical English and Latino sine flexione.Peano's assertion that a math book written in English,German,and Russian is in fact written in Greek-Latin is proven to be correct.展开更多
文摘Based on Conceptual Metaphor Theory(CMT),this paper creates a tiny corpus of ChatGPT-written speeches.Through employing a corpus-driven approach,this study analyzes the identification and utilization of conceptual metaphors in artificial intelligence(AI)languages.The AI demonstrated its capacity to utilize metaphors in the metaphoric corpora through the display of diversity,non-arbitrariness,repetition,and intersectionality in the selection of source domains.It often uses vocabulary combinations with clear similarities to establish metaphorical meaning.In the literal sense,the outcomes of metaphor identification by artificial intelligence differ significantly from those of humans.Therefore,there is a need to develop advanced automatic models for identifying metaphors in order to enhance the precision of metaphor identification consistently.
基金supported by the National Natural Science Foundation of China under grant nos.52277222,52406256,52177217the Shuimu Tsinghua Scholar Program(grant no.2022SM146)an Artificial Intelligence for Research Paradigm Reform Enabling Discipline Leapfrog Program Project Funding Grant.
文摘Rechargeable batteries are pivotal for achieving carbon neutrality and enabling the renewable energy transition.Their advancement requires inno-vations at micro(materials),device(manufacturing),and system(control and optimization)levels.However,traditional trial-and-error approaches are inadequate for modern scientific demands.As a transformative artificial intelligence(AI)technology,large language models(LLMs)deliver powerful semantic understanding and reasoning capabilities,driving a paradigm shift in battery research to address multilevel innovation needs.Neverthe-less,this field still faces dual challenges:ambiguous technical roadmaps and fragmented progress in stage-specific achievements.This review sys-tematically consolidates recent advances in applying LLMs to battery research,distilling core findings across four critical domains:knowledge integration,materials discovery,manufacturing processes,and system management.To address key bottlenecks—including limited model inter-pretability,inadequate alignment with electrochemical mechanisms,and real-world data adaptation challenges—we propose structured frameworks for deep integration of battery research and LLMs,alongside defined future technical pathways.These frameworks bridge fundamental battery science with AI-driven innovation paradigms to facilitate groundbreaking advances in next-generation battery technologies.
文摘The expert system MUST (Mining Under Structures) shown in this paper and established by the authors is a preliminary expert system to solve the policy-making problems for mining under structures by means of computers instead of humanbeing. Based on the experience of relative experts,the authors established a knowledge base about the minings under structures,researched into reasonable method to simulate thinking processes of human experts when they are solving the problems, established the network of an expert system and named it ' MUST system' . MUST system uses the method of the structural system analysis approach. A kind of methods of Turbo Prolog and Fortran 77 language alternations is designed to meet the needs of exchange information within the MUST system. Based on this kind of methods MUST system has been constructed and realised on IBM-PC computer. For verifying the correctness, suitability and reliablity of MUST system,some practical examples of minings under structures were tentatively solved using MUST system,whose results are satisfactory.
文摘This paper introduces the creation of a little-known international auxiliary language by Giuseppe Peano,an Italian mathematician.The language,known as Latino sine flexione,later as Interlingua,was developed as a written language of international communication primarily for use among mathematicians.Based on Latin,the language has a simplified grammatical structure,and its lexicon draws heavily from Latin and European languages.Peano's reflections on the similarities between Latino sine flexione,Chinese,English,and classical Latin are presented,along with some comparisons of translations of Chinese texts into these languages.The paper also examines scientific and mathematical texts to highlight the similarities between technical English and Latino sine flexione.Peano's assertion that a math book written in English,German,and Russian is in fact written in Greek-Latin is proven to be correct.
