Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attack...Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic losses.Since it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are deployed.However,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain contracts.Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution.But the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of detection.This paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert knowledge.In this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from bytecode.First,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model training.Finally,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model.In addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.展开更多
BACKGROUND Breast cancer is one of the most prevalent causes of morbidity and mortality worldwide,presenting an increasing public health challenge,particularly in lowincome and middle-income countries.However,data on ...BACKGROUND Breast cancer is one of the most prevalent causes of morbidity and mortality worldwide,presenting an increasing public health challenge,particularly in lowincome and middle-income countries.However,data on the knowledge,attitudes,and preventive practices regarding breast cancer and the associated factors among females in Wollo,Ethiopia,remain limited.AIM To assess the impact of family history(FH)of breast disease on knowledge,attitudes,and breast cancer preventive practices among reproductive-age females.METHODS A community-based cross-sectional study was conducted in May and June 2022 in Northeast Ethiopia and involved 143 reproductive-age females with FH of breast diseases and 209 without such a history.We selected participants using the systematic random sampling technique.We analyzed the data using Statistical Package for Social Science version 25 software,and logistic regression analysis was employed to determine odds ratios for variable associations,with statistical significance set at P<0.05.RESULTS Among participants with FH of breast diseases,the levels of knowledge,attitudes,and preventive practices were found to be 83.9%[95%confidence interval(CI):77.9-89.9],49.0%(95%CI:40.8-57.1),and 74.1%(95%CI:66.9-81.3),respectively.In contrast,among those without FH of breast diseases,these levels were significantly decreased to 10.5%(95%CI:6.4-14.7),32.1%(95%CI:25.7-38.4),and 16.7%(95%CI:11.7-21.8),respectively.This study also indicated that knowledge,attitudes,and preventive practices related to breast cancer are significantly higher among participants with FH of breast diseases compared to those without HF breast diseases.CONCLUSION Educational status,monthly income,and community health insurance were identified as significant factors associated with the levels of knowledge,attitudes,and preventive practices regarding breast cancer among reproductive-age females.展开更多
Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challeng...Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.展开更多
In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
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
International Journal of Minerals,Metallurgy and Materials is dedicated to the publication and the dissemination of original research articles (and occasional invited reviews) in the fields of Minerals,Metallurgy and ...International Journal of Minerals,Metallurgy and Materials is dedicated to the publication and the dissemination of original research articles (and occasional invited reviews) in the fields of Minerals,Metallurgy and Materials.It is covered by EI Compendex,SCI Expanded,Chemical Abstract,etc.Manuscript preparation The following components are required for a complete manuscript:Title,Author(s),Author affiliation(s),Abstract,Keywords,Main text,Acknowledgements and References.展开更多
Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate...Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.展开更多
In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by pred...In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional strategies.Traditional knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential data.However,these models may neglect crucial short-term dynamics and other relevant features.This paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)architecture.We proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature extractors.This architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key aspects.Empirical evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge states.These results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.展开更多
BACKGROUND Epidemiological surveys indicate an increasing incidence of type 2 diabetes mellitus(T2DM)among children and adolescents worldwide.Due to rapid disease progression,severe long-term cardiorenal complications...BACKGROUND Epidemiological surveys indicate an increasing incidence of type 2 diabetes mellitus(T2DM)among children and adolescents worldwide.Due to rapid disease progression,severe long-term cardiorenal complications,a lack of effective treatment strategies,and substantial socioeconomic burdens,it has become an urgent public health issue that requires management and resolution.Adolescent T2DM differs from adult T2DM.Despite a significant increase in our understanding of youth-onset T2DM over the past two decades,the related review and evidence-based content remain limited.AIM To visualize the hotspots and trends in pediatric and adolescent T2DM research and to forecast their future research themes.METHODS This study utilized the terms“children”,“adolescents”,and“type 2 diabetes”,retrieving relevant articles published between 1983 and 2023 from three citation databases within the Web of Science Core Collection(SCI,SSCI,ESCI).Utilizing CiteSpace and VoSviewer software,we analyze and visually represent the annual output of literature,countries involved,and participating institutions.This allows us to predict trends in this research field.Our analysis encompasses co-cited authors,journal overlays,citation overlays,time-zone views,keyword analysis,and reference analysis,etc.RESULTS A total of 9210 articles were included,and the annual publication volume in this field showed a steady growth trend.The United States had the highest number of publications and the highest H-index.The United States also had the most research institutions and the strongest research capacity.The global hot journals were primarily diabetes professional journals but also included journals related to nutrition,endocrinology,and metabolism.Keyword analysis showed that research related to endothelial dysfunction,exposure risk,cardiac metabolic risk,changes in gut microbiota,the impact on comorbidities and outcomes,etc.,were emerging keywords.They have maintained their popularity in this field,suggesting that these areas have garnered significant research interest in recent years.CONCLUSION Pediatric and adolescent T2DM is increasingly drawing global attention,with genes,behaviors,environmental factors,and multisystemic interventions potentially emerging as future research hot spots.展开更多
BACKGROUND Monkeypox(Mpox),is a disease of global public health concern,as it does not affect only countries in western and central Africa.AIM To assess Burundi healthcare workers(HCWs)s’level of knowledge and confid...BACKGROUND Monkeypox(Mpox),is a disease of global public health concern,as it does not affect only countries in western and central Africa.AIM To assess Burundi healthcare workers(HCWs)s’level of knowledge and confidence in the diagnosis and management of Mpox.METHODS We conducted a cross-sectional study via an online survey designed mainly from the World Health Organization course distributed among Burundi HCWs from June-July 2023.The questionnaire comprises 8 socioprofessional-related questions,22 questions about Mpox disease knowledge,and 3 questions to assess confidence in Mpox diagnosis and management.The data were analyzed via SPSS software version 25.0.A P value<0.05 was considered to indicate statistical significance.RESULTS The study sample comprised 471 HCWs who were mainly medical doctors(63.9%)and nurses(30.1%).None of the 22 questions concerning Mpox knowledge had at least 50%correct responses.A very low number of HCWs(17.4%)knew that Mpox has a vaccine.The confidence level to diagnose(21.20%),treat(18.00%)or prevent(23.30%)Mpox was low among HCWs.The confidence level in the diagnosis of Mpox was associated with the HCWs’age(P value=0.009),sex(P value<0.001),work experience(P value=0.002),and residence(P value<0.001).The confidence level to treat Mpox was significantly associated with the HCWs’age(P value=0.050),sex(P value<0.001),education(P value=0.033)and occupation(P value=0.005).The confidence level to prevent Mpox was associated with the HCWs’education(P value<0.001),work experience(P value=0.002),residence(P value<0.001)and type of work institution(P value=0.003).CONCLUSION This study revealed that HCWs have the lowest level of knowledge regarding Mpox and a lack of confidence in the ability to diagnose,treat or prevent it.There is an urgent need to organize continuing medical education programs on Mpox epidemiology and preparedness for Burundi HCWs.We encourage future researchers to assess potential hesitancy toward Mpox vaccination and its associated factors.展开更多
1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索...1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”。4.本刊于2018年11月第三次荣获中国科协“精品科技期刊工程项目”资助。展开更多
1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索...1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”。展开更多
1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索...1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”。4.本刊于2018年11月第三次荣获中国科协“精品科技期刊工程项目”资助。5.本刊被中国科学技术信息研究所评为2013年―2018年、2020年、2021年、2023年“百种中国杰出学术期刊”称号和“第3届中国精品科技期刊”、“第4届中国精品科技期刊”、“第5届中国精品科技期刊”、“第6届中国精品科技期刊”,即“中国精品科技期刊顶尖学术论文(F-5000)”项目来源期刊,是中国学术期刊编辑委员会《CAJ-CD规范》执行优秀期刊并首批被中国学术期刊评价委员会评为《RCCSE中国权威学术期刊(A+)》。展开更多
Since the beginning of the 21st century,advances in big data and artificial intelligence have driven a paradigm shift in the geosciences,moving the field from qualitative descriptions toward quantitative analysis,from...Since the beginning of the 21st century,advances in big data and artificial intelligence have driven a paradigm shift in the geosciences,moving the field from qualitative descriptions toward quantitative analysis,from observing phenomena to uncovering underlying mechanisms,from regional-scale investigations to global perspectives,and from experience-based inference toward data-and model-enabled intelligent prediction.AlphaEarth Foundations(AEF)is a next-generation geospatial intelligence platform that addresses these changes by introducing a unified 64-dimensional shared embedding space,enabling-for the first time-standardized representation and seamless integration of 12 distinct types of Earth observation data,including optical,radar,and lidar.This framework significantly improves data assimilation efficiency and resolves the persistent problem of“data silos”in geoscience research.AEF is helping redefine research methodologies and fostering breakthroughs,particularly in quantitative Earth system science.This paper systematically examines how AEF’s innovative architecture-featuring multi-source data fusion,high-dimensional feature representation learning,and a scalable computational framework-facilitates intelligent,precise,and realtime data-driven geoscientific research.Using case studies from resource and environmental applications,we demonstrate AEF’s broad potential and identify emerging innovation needs.Our findings show that AEF not only enhances the efficiency of solving traditional geoscientific problems but also stimulates novel research directions and methodological approaches.展开更多
1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索...1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”。4.本刊于2018年11月第三次荣获中国科协“精品科技期刊工程项目”资助。展开更多
1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索...1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”。4.本刊于2018年11月第三次荣获中国科协“精品科技期刊工程项目”资助。展开更多
1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索...1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”。4.本刊于2018年11月第三次荣获中国科协“精品科技期刊工程项目”资助。展开更多
1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索...1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”展开更多
This article revisits the concept of epistemological rupture by questioning the stark division between scientific and non-scientific thought. Drawing on the theory of representation, it contends that both forms of kno...This article revisits the concept of epistemological rupture by questioning the stark division between scientific and non-scientific thought. Drawing on the theory of representation, it contends that both forms of knowledge are socially constructed, moulded by communication, norms and group dynamics. Rather than labelling non-scientific thought as flawed or regressive, the discussion shows how decontextualization and recontextualization processes apply equally to everyday ‘natural' knowledge and formal science,exposing the social and historical contingencies shaping concepts. Consequently, rupture appears less a sudden break than a gradual threshold reached through dialectical transformations in cognition and society. Rather than conferring total superiority on science, ruptures highlight how certain discourses gain legitimacy while others become ‘non-knowledge'. The article concludes that science's dominance reflects broader power relationships and evolving modes of production and validation. By situating epistemological rupture within these processes, it illuminates how different knowledge forms coexist, evolve and sometimes conflict in stratified social fields—ultimately challenging a simplistic binary between scientific progress and supposedly primitive or natural thought. This viewpoint opens new possibilities for examining the shifting boundaries between rational explanations and the shared beliefs shaping collective reality and daily life.展开更多
The article is based on language model,through the cue word engineering and agent thinking method,automatic knowledge extraction,with China accounting standards support to complete the corresponding knowledge map cons...The article is based on language model,through the cue word engineering and agent thinking method,automatic knowledge extraction,with China accounting standards support to complete the corresponding knowledge map construction.Through the way of extracting the accounting entities and their connections in the pattern layer,the data layer is provided for the fine-tuning and optimization of the large model.Studies found that,through the reasonable application of language model,knowledge can be realized in massive financial data neural five effective extracted tuples,and complete accounting knowledge map construction.展开更多
基金partially supported by the National Natural Science Foundation (62272248)the Open Project Fund of State Key Laboratory of Computer Architecture,Institute of Computing Technology,Chinese Academy of Sciences (CARCHA202108,CARCH201905)+1 种基金the Natural Science Foundation of Tianjin (20JCZDJC00610)Sponsored by Zhejiang Lab (2021KF0AB04)。
文摘Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on Ethereum.Effective vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic losses.Since it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are deployed.However,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain contracts.Learning-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol execution.But the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of detection.This paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert knowledge.In this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from bytecode.First,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model training.Finally,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection model.In addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.
文摘BACKGROUND Breast cancer is one of the most prevalent causes of morbidity and mortality worldwide,presenting an increasing public health challenge,particularly in lowincome and middle-income countries.However,data on the knowledge,attitudes,and preventive practices regarding breast cancer and the associated factors among females in Wollo,Ethiopia,remain limited.AIM To assess the impact of family history(FH)of breast disease on knowledge,attitudes,and breast cancer preventive practices among reproductive-age females.METHODS A community-based cross-sectional study was conducted in May and June 2022 in Northeast Ethiopia and involved 143 reproductive-age females with FH of breast diseases and 209 without such a history.We selected participants using the systematic random sampling technique.We analyzed the data using Statistical Package for Social Science version 25 software,and logistic regression analysis was employed to determine odds ratios for variable associations,with statistical significance set at P<0.05.RESULTS Among participants with FH of breast diseases,the levels of knowledge,attitudes,and preventive practices were found to be 83.9%[95%confidence interval(CI):77.9-89.9],49.0%(95%CI:40.8-57.1),and 74.1%(95%CI:66.9-81.3),respectively.In contrast,among those without FH of breast diseases,these levels were significantly decreased to 10.5%(95%CI:6.4-14.7),32.1%(95%CI:25.7-38.4),and 16.7%(95%CI:11.7-21.8),respectively.This study also indicated that knowledge,attitudes,and preventive practices related to breast cancer are significantly higher among participants with FH of breast diseases compared to those without HF breast diseases.CONCLUSION Educational status,monthly income,and community health insurance were identified as significant factors associated with the levels of knowledge,attitudes,and preventive practices regarding breast cancer among reproductive-age females.
基金supported by Natural Science Foundation of Sichuan,China(Grant No.:2024ZDZX0019).
文摘Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
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.
文摘International Journal of Minerals,Metallurgy and Materials is dedicated to the publication and the dissemination of original research articles (and occasional invited reviews) in the fields of Minerals,Metallurgy and Materials.It is covered by EI Compendex,SCI Expanded,Chemical Abstract,etc.Manuscript preparation The following components are required for a complete manuscript:Title,Author(s),Author affiliation(s),Abstract,Keywords,Main text,Acknowledgements and References.
基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDB0740000National Key Research and Development Program of China,No.2022YFB3904200,No.2022YFF0711601+1 种基金Key Project of Innovation LREIS,No.PI009National Natural Science Foundation of China,No.42471503。
文摘Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.
基金supported by National Natural Science Foundation of China(Nos.62266054 and 62166050)Key Program of Fundamental Research Project of Yunnan Science and Technology Plan(No.202201AS070021)+2 种基金Yunnan Fundamental Research Projects(No.202401AT070122)Yunnan International Joint Research and Development Center of China-Laos-Thailand Educational Digitalization(No.202203AP140006)Scientific Research Foundation of Yunnan Provincial Department of Education(No.2024Y159).
文摘In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional strategies.Traditional knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential data.However,these models may neglect crucial short-term dynamics and other relevant features.This paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)architecture.We proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature extractors.This architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key aspects.Empirical evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge states.These results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.
基金Supported by the National Natural Science Foundation of China,No.82105018 and No.81903950.
文摘BACKGROUND Epidemiological surveys indicate an increasing incidence of type 2 diabetes mellitus(T2DM)among children and adolescents worldwide.Due to rapid disease progression,severe long-term cardiorenal complications,a lack of effective treatment strategies,and substantial socioeconomic burdens,it has become an urgent public health issue that requires management and resolution.Adolescent T2DM differs from adult T2DM.Despite a significant increase in our understanding of youth-onset T2DM over the past two decades,the related review and evidence-based content remain limited.AIM To visualize the hotspots and trends in pediatric and adolescent T2DM research and to forecast their future research themes.METHODS This study utilized the terms“children”,“adolescents”,and“type 2 diabetes”,retrieving relevant articles published between 1983 and 2023 from three citation databases within the Web of Science Core Collection(SCI,SSCI,ESCI).Utilizing CiteSpace and VoSviewer software,we analyze and visually represent the annual output of literature,countries involved,and participating institutions.This allows us to predict trends in this research field.Our analysis encompasses co-cited authors,journal overlays,citation overlays,time-zone views,keyword analysis,and reference analysis,etc.RESULTS A total of 9210 articles were included,and the annual publication volume in this field showed a steady growth trend.The United States had the highest number of publications and the highest H-index.The United States also had the most research institutions and the strongest research capacity.The global hot journals were primarily diabetes professional journals but also included journals related to nutrition,endocrinology,and metabolism.Keyword analysis showed that research related to endothelial dysfunction,exposure risk,cardiac metabolic risk,changes in gut microbiota,the impact on comorbidities and outcomes,etc.,were emerging keywords.They have maintained their popularity in this field,suggesting that these areas have garnered significant research interest in recent years.CONCLUSION Pediatric and adolescent T2DM is increasingly drawing global attention,with genes,behaviors,environmental factors,and multisystemic interventions potentially emerging as future research hot spots.
文摘BACKGROUND Monkeypox(Mpox),is a disease of global public health concern,as it does not affect only countries in western and central Africa.AIM To assess Burundi healthcare workers(HCWs)s’level of knowledge and confidence in the diagnosis and management of Mpox.METHODS We conducted a cross-sectional study via an online survey designed mainly from the World Health Organization course distributed among Burundi HCWs from June-July 2023.The questionnaire comprises 8 socioprofessional-related questions,22 questions about Mpox disease knowledge,and 3 questions to assess confidence in Mpox diagnosis and management.The data were analyzed via SPSS software version 25.0.A P value<0.05 was considered to indicate statistical significance.RESULTS The study sample comprised 471 HCWs who were mainly medical doctors(63.9%)and nurses(30.1%).None of the 22 questions concerning Mpox knowledge had at least 50%correct responses.A very low number of HCWs(17.4%)knew that Mpox has a vaccine.The confidence level to diagnose(21.20%),treat(18.00%)or prevent(23.30%)Mpox was low among HCWs.The confidence level in the diagnosis of Mpox was associated with the HCWs’age(P value=0.009),sex(P value<0.001),work experience(P value=0.002),and residence(P value<0.001).The confidence level to treat Mpox was significantly associated with the HCWs’age(P value=0.050),sex(P value<0.001),education(P value=0.033)and occupation(P value=0.005).The confidence level to prevent Mpox was associated with the HCWs’education(P value<0.001),work experience(P value=0.002),residence(P value<0.001)and type of work institution(P value=0.003).CONCLUSION This study revealed that HCWs have the lowest level of knowledge regarding Mpox and a lack of confidence in the ability to diagnose,treat or prevent it.There is an urgent need to organize continuing medical education programs on Mpox epidemiology and preparedness for Burundi HCWs.We encourage future researchers to assess potential hesitancy toward Mpox vaccination and its associated factors.
文摘1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”。4.本刊于2018年11月第三次荣获中国科协“精品科技期刊工程项目”资助。
文摘1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”。
文摘1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”。4.本刊于2018年11月第三次荣获中国科协“精品科技期刊工程项目”资助。5.本刊被中国科学技术信息研究所评为2013年―2018年、2020年、2021年、2023年“百种中国杰出学术期刊”称号和“第3届中国精品科技期刊”、“第4届中国精品科技期刊”、“第5届中国精品科技期刊”、“第6届中国精品科技期刊”,即“中国精品科技期刊顶尖学术论文(F-5000)”项目来源期刊,是中国学术期刊编辑委员会《CAJ-CD规范》执行优秀期刊并首批被中国学术期刊评价委员会评为《RCCSE中国权威学术期刊(A+)》。
基金National Natural Science Foundation of China Key Project(No.42050103)Higher Education Disciplinary Innovation Program(No.B25052)+2 种基金the Guangdong Pearl River Talent Program Innovative and Entrepreneurial Team Project(No.2021ZT09H399)the Ministry of Education’s Frontiers Science Center for Deep-Time Digital Earth(DDE)(No.2652023001)Geological Survey Project of China Geological Survey(DD20240206201)。
文摘Since the beginning of the 21st century,advances in big data and artificial intelligence have driven a paradigm shift in the geosciences,moving the field from qualitative descriptions toward quantitative analysis,from observing phenomena to uncovering underlying mechanisms,from regional-scale investigations to global perspectives,and from experience-based inference toward data-and model-enabled intelligent prediction.AlphaEarth Foundations(AEF)is a next-generation geospatial intelligence platform that addresses these changes by introducing a unified 64-dimensional shared embedding space,enabling-for the first time-standardized representation and seamless integration of 12 distinct types of Earth observation data,including optical,radar,and lidar.This framework significantly improves data assimilation efficiency and resolves the persistent problem of“data silos”in geoscience research.AEF is helping redefine research methodologies and fostering breakthroughs,particularly in quantitative Earth system science.This paper systematically examines how AEF’s innovative architecture-featuring multi-source data fusion,high-dimensional feature representation learning,and a scalable computational framework-facilitates intelligent,precise,and realtime data-driven geoscientific research.Using case studies from resource and environmental applications,we demonstrate AEF’s broad potential and identify emerging innovation needs.Our findings show that AEF not only enhances the efficiency of solving traditional geoscientific problems but also stimulates novel research directions and methodological approaches.
文摘1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”。4.本刊于2018年11月第三次荣获中国科协“精品科技期刊工程项目”资助。
文摘1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”。4.本刊于2018年11月第三次荣获中国科协“精品科技期刊工程项目”资助。
文摘1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”。4.本刊于2018年11月第三次荣获中国科协“精品科技期刊工程项目”资助。
文摘1.《工程力学》是中国科学技术协会主管、中国力学学会主办、清华大学土木工程系承办的学术期刊。2.本刊为Ei Compendex、Scopus及ISI Web of Knowledge文摘数据库收录期刊。万方数据库、中国知网、重庆维普、超星、EBSCO数据库全文检索。3.本刊于2019年和2024年两度入选“中国科技期刊卓越行动计划梯队项目”
文摘This article revisits the concept of epistemological rupture by questioning the stark division between scientific and non-scientific thought. Drawing on the theory of representation, it contends that both forms of knowledge are socially constructed, moulded by communication, norms and group dynamics. Rather than labelling non-scientific thought as flawed or regressive, the discussion shows how decontextualization and recontextualization processes apply equally to everyday ‘natural' knowledge and formal science,exposing the social and historical contingencies shaping concepts. Consequently, rupture appears less a sudden break than a gradual threshold reached through dialectical transformations in cognition and society. Rather than conferring total superiority on science, ruptures highlight how certain discourses gain legitimacy while others become ‘non-knowledge'. The article concludes that science's dominance reflects broader power relationships and evolving modes of production and validation. By situating epistemological rupture within these processes, it illuminates how different knowledge forms coexist, evolve and sometimes conflict in stratified social fields—ultimately challenging a simplistic binary between scientific progress and supposedly primitive or natural thought. This viewpoint opens new possibilities for examining the shifting boundaries between rational explanations and the shared beliefs shaping collective reality and daily life.
文摘The article is based on language model,through the cue word engineering and agent thinking method,automatic knowledge extraction,with China accounting standards support to complete the corresponding knowledge map construction.Through the way of extracting the accounting entities and their connections in the pattern layer,the data layer is provided for the fine-tuning and optimization of the large model.Studies found that,through the reasonable application of language model,knowledge can be realized in massive financial data neural five effective extracted tuples,and complete accounting knowledge map construction.
