Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and di...Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.展开更多
Based on 1,003 articles about empirical research on interpreting teaching from 2002 to 2022 retrieved from China National Knowledge Internet,this paper extracts three main research methods,uncovering common problems i...Based on 1,003 articles about empirical research on interpreting teaching from 2002 to 2022 retrieved from China National Knowledge Internet,this paper extracts three main research methods,uncovering common problems in interpreting education and practical teaching suggestions:(1)Corpus-based researches collect numerous audios to study typical mistakes made by interpreting learners,particularly pause and self-repair,and suggest interpreting teaching improve learners’ability to use language chunks and encourage students to interpret smoothly;(2)Questionnaire surveys help understand requirements for professional interpreters and how interpreting teaching meets market demands;(3)Teaching experiments last for one to two semesters,addressing issues like outdated teaching materials and modes,and show how teaching materials and modes integrate modern technology.But empirical researches need to build new corpora,professional interpreters’corpora and address problems that haven’t been adequately discussed.This paper is helpful for improving interpreting education in China and other countries and for making clear tasks to be fulfilled in empirical research on interpreting education.展开更多
This study investigates the diversity of gut microbiota in Metaphire peguana,an earthworm species commonly found in agricultural areas of Thailand.Earthworms play a critical role in soil ecosystems by supporting nutri...This study investigates the diversity of gut microbiota in Metaphire peguana,an earthworm species commonly found in agricultural areas of Thailand.Earthworms play a critical role in soil ecosystems by supporting nutrient cycling and breaking down organic matter.Understanding the microbial diversity in their gut is essential for exploring their ecological contributions.Using Next Generation Sequencing(NGS),we analyzed the mycobiome in the gut of M.peguana.Our findings revealed a high diversity of fungal species,primarily belonging to two major phyla:Ascomycota and Basidiomycota.Ascomycota was the most abundant phylum,comprising 40.1% of the total fungal species identified.A total of 33 distinct fungal species were identified,which underscores the richness of microbial life within the earthworm gut.This study successfully created the first genetic database of the microbial community in M.peguana,providing a foundation for future research in agricultural applications.The microbial species identified,particularly siderophoreproducing fungi,could have significant implications for improving soil fertility and promoting sustainable agricultural practices.The use of NGS technology has enabled comprehensive profiling of microbial communities,allowing for precise identification of fungi that may play essential roles in soil health.Furthermore,the study paves the way for future studies on the potential applications of earthworm gut microbiomes in biotechnology,especially in enhancing soil nutrient availability and plant growth.The findings of this research contribute to the broader understanding of the ecological roles of earthworms and their microbiomes in soil ecosystems.展开更多
This paper explores the paradigm reconstruction of interpreting pedagogy driven by generative AI technology.With the breakthroughs of AI technologies such as ChatGPT in natural language processing,traditional interpre...This paper explores the paradigm reconstruction of interpreting pedagogy driven by generative AI technology.With the breakthroughs of AI technologies such as ChatGPT in natural language processing,traditional interpreting education faces dual challenges of technological substitution and pedagogical transformation.Based on Kuhn’s paradigm theory,the study analyzes the limitations of three traditional interpreting teaching paradigms,language-centric,knowledge-based,and skill-acquisition-oriented,and proposes a novel“teacher-AI-learner”triadic collaborative paradigm.Through reconstructing teaching subjects,environments,and curriculum systems,the integration of real-time translation tools and intelligent terminology databases facilitates the transition from static skill training to dynamic human-machine collaboration.The research simultaneously highlights challenges in technological ethics and curriculum design transformation pressures,emphasizing the necessity to balance technological empowerment with humanistic education.展开更多
Interpreting is a fast-paced activity where interpreters must make quick choices when faced with uncertainty. This study looks at how professional interpreters handle linguistic uncertainty in English-Chinese sight tr...Interpreting is a fast-paced activity where interpreters must make quick choices when faced with uncertainty. This study looks at how professional interpreters handle linguistic uncertainty in English-Chinese sight translation, with a focus on the strategies they use. By analyzing transcription data alongside instructor evaluations, we found that interpreters relied most on creative interpretation and omission, while strategies like paraphrasing, simplification, transformation, addition, and generalization appeared less often. The results show a clear preference for strategies that keep communication flowing without adding unnecessary cognitive load. These findings support the Processing Economy Hypothesis, which suggests interpreters naturally seek efficient ways to process language while maintaining meaning. The study also highlights practical implications for interpreter training, emphasizing the value of flexible, economy-oriented strategies to help interpreters stay fluent under pressure.展开更多
The rapid advancement of artificial intelligence(AI)technologies has fundamentally transformed various sectors of society,with education being no exception.This paper examines interpreting education in the AI era,anal...The rapid advancement of artificial intelligence(AI)technologies has fundamentally transformed various sectors of society,with education being no exception.This paper examines interpreting education in the AI era,analyzing both the unprecedented opportunities and formidable challenges that educators and institutions face.Through a review of recent literature and theoretical analysis,this study explores how AI technologies are reshaping interpreter training methodologies,assessment practices,and professional competencies.The paper argues that while AI presents remarkable opportunities for enhanced learning experiences and personalized education,it simultaneously poses significant challenges related to pedagogical adaptation,ethical considerations,and the preservation of human-centric skills essential to professional interpreting.It suggests that successful integration of AI in interpreting education requires a balanced approach that leverages technological advantages while maintaining the irreplaceable value of human expertise and cultural sensitivity.展开更多
目的:对“ICU病人体位管理与早期活动指南”(Guideline on positioning and early mobilisation in the critically ill by an expert panel)进行系统解读。方法:系统梳理该《指南》的制定背景、证据质量与推荐要点,重点解读其中关于体...目的:对“ICU病人体位管理与早期活动指南”(Guideline on positioning and early mobilisation in the critically ill by an expert panel)进行系统解读。方法:系统梳理该《指南》的制定背景、证据质量与推荐要点,重点解读其中关于体位管理与早期活动的核心建议,并据此提出符合我国国情的临床实践思考。结果:《指南》共提出46条建议,包括体位管理23条、早期活动19条、辅助设备4条、神经肌肉电刺激2条。核心推荐:气管插管病人床头抬高≥40°;急性呼吸窘迫综合征(ARDS)病人氧合指数<150 mmHg时尽早实施俯卧位通气(至少12 h);ICU病人入院72 h内启动早期活动。结论:《指南》为ICU病人体位管理和早期活动提供循证依据。结合我国国情,建议加强医护人员培训、建立多学科协作机制、完善评估监测体系,确保规范实施。展开更多
针对中老年女性骨质疏松(osteoporosis,OP)患病率高而基层医疗机构筛查手段不足的问题,利用多中心电子健康记录数据与机器学习技术构建中老年女性骨质疏松两阶段筛查模型(integration of categorical boosting and attentive interpreta...针对中老年女性骨质疏松(osteoporosis,OP)患病率高而基层医疗机构筛查手段不足的问题,利用多中心电子健康记录数据与机器学习技术构建中老年女性骨质疏松两阶段筛查模型(integration of categorical boosting and attentive interpretable tabular learning for osteoporosis screenig,OP-CatNet).初步筛查阶段采用分类提升树(categorical boosting,CatBoost)模型基于个人健康数据进行骨质疏松初步筛查,准确率达到86.88%,敏感性为81.19%,特异性为88.71%,显示出良好的筛查效果.在深化筛查阶段,采用表格网络(attentive interpretable tabular leavning,TabNet)模型结合实验室检查数据与初步筛查决策,准确率达到92.06%,敏感性提升至81.41%,特异性达95.41%,筛查性能明显提升.此外,深化筛查阶段利用夏普利加性解释(SHapley additive exPlanations,SHAP)方法进行模型的全局可解释性分析,结合TabNet的局部可解释性特点,使模型预测结果更具透明度和可信度.展开更多
基金Deep-time Digital Earth(DDE)Big Science Program(No.GJ-C03-SGF-2025-004)National Natural Science Foundation of China(No.42394063)Sichuan Science and Technology Program(No.2025ZNSFSC0325).
文摘Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.
基金USST Construction Project of English-taught Courses for International Students in 2024Key Course Construction Project in Universities of Shanghai in 2024USST Teaching Achievement Award(postgraduate)Cultivation Project in 2024。
文摘Based on 1,003 articles about empirical research on interpreting teaching from 2002 to 2022 retrieved from China National Knowledge Internet,this paper extracts three main research methods,uncovering common problems in interpreting education and practical teaching suggestions:(1)Corpus-based researches collect numerous audios to study typical mistakes made by interpreting learners,particularly pause and self-repair,and suggest interpreting teaching improve learners’ability to use language chunks and encourage students to interpret smoothly;(2)Questionnaire surveys help understand requirements for professional interpreters and how interpreting teaching meets market demands;(3)Teaching experiments last for one to two semesters,addressing issues like outdated teaching materials and modes,and show how teaching materials and modes integrate modern technology.But empirical researches need to build new corpora,professional interpreters’corpora and address problems that haven’t been adequately discussed.This paper is helpful for improving interpreting education in China and other countries and for making clear tasks to be fulfilled in empirical research on interpreting education.
文摘This study investigates the diversity of gut microbiota in Metaphire peguana,an earthworm species commonly found in agricultural areas of Thailand.Earthworms play a critical role in soil ecosystems by supporting nutrient cycling and breaking down organic matter.Understanding the microbial diversity in their gut is essential for exploring their ecological contributions.Using Next Generation Sequencing(NGS),we analyzed the mycobiome in the gut of M.peguana.Our findings revealed a high diversity of fungal species,primarily belonging to two major phyla:Ascomycota and Basidiomycota.Ascomycota was the most abundant phylum,comprising 40.1% of the total fungal species identified.A total of 33 distinct fungal species were identified,which underscores the richness of microbial life within the earthworm gut.This study successfully created the first genetic database of the microbial community in M.peguana,providing a foundation for future research in agricultural applications.The microbial species identified,particularly siderophoreproducing fungi,could have significant implications for improving soil fertility and promoting sustainable agricultural practices.The use of NGS technology has enabled comprehensive profiling of microbial communities,allowing for precise identification of fungi that may play essential roles in soil health.Furthermore,the study paves the way for future studies on the potential applications of earthworm gut microbiomes in biotechnology,especially in enhancing soil nutrient availability and plant growth.The findings of this research contribute to the broader understanding of the ecological roles of earthworms and their microbiomes in soil ecosystems.
基金2025 General Project of Humanities and Social Sciences Research in Henan Higher Education Institutions,“Research on the Dynamic Mechanisms and Paths of Innovative Development of Undergraduate Translation Programs Empowered by New Productive Forces”(Project No.:2025-ZDJH-885)2024 College-Level Undergraduate Teaching Reform Project of the School of Foreign Languages,Henan University of Technology,“Research on Implementation Paths of New Models for Interpreter Training Based on AI Large Models”(Project No.:2024YJWYJG06)+1 种基金2025 First-Class Undergraduate Program Construction Special Project of the School of Foreign Languages,Henan University of Technology,titled“Research on Development Paths for Innovative Development of Undergraduate Translation Programs Empowered by New Productive Forces”(Project No.:2025WYZYJS30)2025 Educational Reform Project of the School of International Education,Henan University of Technology,“A Study on the Language Competence Development Model for International Talents Based on the Al Large Model-Taking IELTS Reading and Writing Teaching Practice as an Example”(Project No.:GJXY202533)。
文摘This paper explores the paradigm reconstruction of interpreting pedagogy driven by generative AI technology.With the breakthroughs of AI technologies such as ChatGPT in natural language processing,traditional interpreting education faces dual challenges of technological substitution and pedagogical transformation.Based on Kuhn’s paradigm theory,the study analyzes the limitations of three traditional interpreting teaching paradigms,language-centric,knowledge-based,and skill-acquisition-oriented,and proposes a novel“teacher-AI-learner”triadic collaborative paradigm.Through reconstructing teaching subjects,environments,and curriculum systems,the integration of real-time translation tools and intelligent terminology databases facilitates the transition from static skill training to dynamic human-machine collaboration.The research simultaneously highlights challenges in technological ethics and curriculum design transformation pressures,emphasizing the necessity to balance technological empowerment with humanistic education.
基金this paper was supported by Humanities and Social Science Youth Foundation of Ministry of Education,China[23YJC740018]。
文摘Interpreting is a fast-paced activity where interpreters must make quick choices when faced with uncertainty. This study looks at how professional interpreters handle linguistic uncertainty in English-Chinese sight translation, with a focus on the strategies they use. By analyzing transcription data alongside instructor evaluations, we found that interpreters relied most on creative interpretation and omission, while strategies like paraphrasing, simplification, transformation, addition, and generalization appeared less often. The results show a clear preference for strategies that keep communication flowing without adding unnecessary cognitive load. These findings support the Processing Economy Hypothesis, which suggests interpreters naturally seek efficient ways to process language while maintaining meaning. The study also highlights practical implications for interpreter training, emphasizing the value of flexible, economy-oriented strategies to help interpreters stay fluent under pressure.
文摘The rapid advancement of artificial intelligence(AI)technologies has fundamentally transformed various sectors of society,with education being no exception.This paper examines interpreting education in the AI era,analyzing both the unprecedented opportunities and formidable challenges that educators and institutions face.Through a review of recent literature and theoretical analysis,this study explores how AI technologies are reshaping interpreter training methodologies,assessment practices,and professional competencies.The paper argues that while AI presents remarkable opportunities for enhanced learning experiences and personalized education,it simultaneously poses significant challenges related to pedagogical adaptation,ethical considerations,and the preservation of human-centric skills essential to professional interpreting.It suggests that successful integration of AI in interpreting education requires a balanced approach that leverages technological advantages while maintaining the irreplaceable value of human expertise and cultural sensitivity.
文摘目的:对“ICU病人体位管理与早期活动指南”(Guideline on positioning and early mobilisation in the critically ill by an expert panel)进行系统解读。方法:系统梳理该《指南》的制定背景、证据质量与推荐要点,重点解读其中关于体位管理与早期活动的核心建议,并据此提出符合我国国情的临床实践思考。结果:《指南》共提出46条建议,包括体位管理23条、早期活动19条、辅助设备4条、神经肌肉电刺激2条。核心推荐:气管插管病人床头抬高≥40°;急性呼吸窘迫综合征(ARDS)病人氧合指数<150 mmHg时尽早实施俯卧位通气(至少12 h);ICU病人入院72 h内启动早期活动。结论:《指南》为ICU病人体位管理和早期活动提供循证依据。结合我国国情,建议加强医护人员培训、建立多学科协作机制、完善评估监测体系,确保规范实施。
