Purpose:This paper compares the paradigmatic differences between knowledge organization(KO)in library and information science and knowledge representation(KR)in AI to show the convergence in KO and KR methods and appl...Purpose:This paper compares the paradigmatic differences between knowledge organization(KO)in library and information science and knowledge representation(KR)in AI to show the convergence in KO and KR methods and applications.Methodology:The literature review and comparative analysis of KO and KR paradigms is the primary method used in this paper.Findings:A key difference between KO and KR lays in the purpose of KO is to organize knowledge into certain structure for standardizing and/or normalizing the vocabulary of concepts and relations,while KR is problem-solving oriented.Differences between KO and KR are discussed based on the goal,methods,and functions.Research limitations:This is only a preliminary research with a case study as proof of concept.Practical implications:The paper articulates on the opportunities in applying KR and other AI methods and techniques to enhance the functions of KO.Originality/value:Ontologies and linked data as the evidence of the convergence of KO and KR paradigms provide theoretical and methodological support to innovate KO in the AI era.展开更多
In the big data environment, the construction of massive S&T literature data resources needs intelligent technical assistance. With a focus on comparing the domestic and foreign knowledge organization systems and ...In the big data environment, the construction of massive S&T literature data resources needs intelligent technical assistance. With a focus on comparing the domestic and foreign knowledge organization systems and their applications, this article analyzes and summarizes the gaps in related researches and applications at home and abroad. A knowledge organization system framework for S&T literature data resources is presented in the article. Starting from the basic element of knowledge organization system, it also proposes and designs terminology-based analysis methods and technologies for S&T literature. Based on this framework, it proposes ideas and develops corresponding software tool to carry out relevant experiments. It gives an overview of theories and technologies method for future research.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
Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predict...Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.展开更多
Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI pre...Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.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
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
The integration of digital tools and effective knowledge management practices is critical for enhancing administrative efficiency and institutional continuity in higher education. This study investigates the relations...The integration of digital tools and effective knowledge management practices is critical for enhancing administrative efficiency and institutional continuity in higher education. This study investigates the relationships between knowledge modeling, institutional memory, leadership styles, technology, and administrative efficiency at the University of Cape Coast (UCC). The study sought to identify the challenges and opportunities in integrating digital tools into administrative processes and to provide actionable recommendations for improvement. A mixed-methods research design was employed, combining quantitative analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) with qualitative thematic analysis of interviews. The findings revealed key challenges, including resistance to change, fragmented knowledge repositories, and inadequate funding, alongside opportunities such as centralized knowledge systems, cost-effective open-source tools, and capacity-building initiatives. The study highlights the importance of strategic leadership, robust policies, and investments in digital infrastructure to enhance administrative practices. Policy implications include the need for clear digital transformation guidelines and leadership training to foster innovation and collaboration. Recommendations include investing in scalable digital tools, implementing comprehensive capacity-building programs, and promoting stakeholder engagement to drive successful digital integration. These insights provide a roadmap for UCC and similar institutions seeking to optimize administrative efficiency through digital transformation.展开更多
This study explores the epistemic imperative to decolonize African education systems by centering indigenous philosophies such as Ubuntu and introducing the Ubuntu Pedagogy as a pedagogical model.Ubuntu pedagogy trans...This study explores the epistemic imperative to decolonize African education systems by centering indigenous philosophies such as Ubuntu and introducing the Ubuntu Pedagogy as a pedagogical model.Ubuntu pedagogy transforms teacher-learner relationships,it provides a replicable model for relational learning,community partnerships,and reassert the dignity of indigenous epistemologies.The paper examines how language,knowledge production,and pedagogy can be restructured to reflect African epistemologies and educational sovereignty.This research also explores the relationship between mother tongue instruction and cognitive access to learning.Through a qualitative literature analysis of case studies and African scholarly discourse,this paper highlights the continued marginalization of indigenous knowledge systems and the need to embed culturally relevant teaching methodologies.The findings support the broader question of whether there exists an epistemological base for knowledge independence or production within African and Afro-Diasporic contexts,revealing culturally coherent frameworks of learning that resist colonial dominance and an exploration of reclaiming African indigenous knowledge systems for educational and cultural sovereignty.展开更多
Indigenous cultures prescribed a means of maximizing the benefits they produced and enjoyed in their relationship with each other and the environment-based on their understanding of the nature of existence and how to ...Indigenous cultures prescribed a means of maximizing the benefits they produced and enjoyed in their relationship with each other and the environment-based on their understanding of the nature of existence and how to live in harmony with the forces shaping the nature of existence.The emergence of civilization introduced the claim that rational abilities superseded indigenous knowledge.This was followed by positivism and the claim that knowledge passed through three stages:mythological,philosophical,and scientific.This impacted indigenous cultures in ways that reached a height when postcolonial development experts convinced national leaders that progress required adopting advances in science.A failure to modernize was regarded as holding back progress.With the development paradigm now regarded as inadequate for achieving its goals and with the rise of the sustainability discourse,there is appreciation for indigenous knowledge.This article describes an indigenous cultural knowledge system that reflects the insight and wisdom of the world’s most respected scientific and philosophical traditions.The beliefs of the Bodo people of Northeast India are used as an example of an indigenous worldview that portrays insight proven to have value that is comparable to the natural sciences,plus theories of natural law and political philosophy.展开更多
Objective:This study aimed to explore undergraduates’knowledge,attitude,and practice/behavior of human papillomavirus(HPV)vaccination,as well as the essential influencing factors for vaccination decision-making.Metho...Objective:This study aimed to explore undergraduates’knowledge,attitude,and practice/behavior of human papillomavirus(HPV)vaccination,as well as the essential influencing factors for vaccination decision-making.Methods:Through cluster and convenience sampling,2000 undergraduates from the Nursing and Language department of a university in Shanghai were sent a self-designed questionnaire.Chi-square tests,independent sample t-test/ANOVE,and multiple linear regression were used to investigate participants’knowledge and attitude on HPV vaccination,as well as the factors that predicted potential action to receive and promote HPV vaccination in the future.Results:The mean HPV knowledge score was 5.027 out of 10.Health science students showed a significantly higher knowledge mean score than the non-health science students(P<0.000).There was a statistically difference in HPV vaccination attitude among undergraduates in different grades(P<0.05).Awareness of cervical cancer and worries about the risk of cervical cancer were the significant predictors of willingness to receive and promote HPV vaccination in the future.Conclusions:It would take time for a new health product to be aware,understood,accepted,and received.Education providing and information sharing are expected to break the dawn and make the procedure processed.展开更多
Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite a...Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.展开更多
The purpose of this study was to examine the knowledge,attitude,motivation and behavior of the community before and after the experiment,and also to determine the effect of the experiment on increasing knowledge,attit...The purpose of this study was to examine the knowledge,attitude,motivation and behavior of the community before and after the experiment,and also to determine the effect of the experiment on increasing knowledge,attitude,motivation,and behavior related to the construction of family toilets in coastal areas.The study was conducted in Pangkep and Maros Regencies.Atotal of 50 heads of families were selected as participants using the purposive sampling method.25 participants became the experimental group and 25 people became the control group.The research variables included knowledge,attitudes,motivation,and behavior of the community in building family toilets before and after the experiment.Data collection through tests,questionnaires,and observations to each participant.The research instruments were knowledge tests,questionnaires,and observations.Data analysis used descriptive and inferential statistical analysis,with the t-test.The results of the study showed that based on the experiment,knowledge had a significant effect with a correlation coefficient of 0.94,attitudes had an effect of 0.91,motivation was 0.756,and behavior was 0.865.It can be concluded that the construction of family toilets in the coastal areas of Pangkep and Maros Regencies,before the experiment,the knowledge,attitudes,motivation,and behavior of the community were in the low category,and after the experiment increased significantly to the high category. In addition, the results of the analysis showed that the experiment had a significant effect on increasing theknowledge, attitudes, motivation, and behavior of the community towards the construction of family toilets in coastal areas.展开更多
This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electroca...This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.展开更多
Background: Blood transfusion (BT) is crucial to the provision of modern health care. However, blood is scarce and costly, and its use is associated with risks. Therefore, the medical professionals who handle it shoul...Background: Blood transfusion (BT) is crucial to the provision of modern health care. However, blood is scarce and costly, and its use is associated with risks. Therefore, the medical professionals who handle it should have adequate knowledge to ensure rational and safe utilization. The objective of the study was to determine the level of BT knowledge among junior medical doctors in Kenya. Methodology: A cross-sectional study was conducted among junior medical doctors working in Western Kenya. Data was collected using questionnaires from August 2021 to March 2022, and analysis was done by way of descriptive and inferential statistics. A p Results: A total of 150 medical doctors participated in the study. Males comprised 60% (n = 90), and the mean age of the participants was 29.9 (SD 3.6) with a range of 25 - 45 years. The mean knowledge score was 54.1% ± 16.4% and was associated with orientation (AOR = 3.157, 95% CI = 1.194 - 8.337). Conclusion: Blood transfusion knowledge among the doctors was suboptimal and was associated with pre-internship induction. There is a need for additional education in BT during all phases of medical training and practice, including orientation for medical interns.展开更多
BACKGROUND Adults with type 2 diabetes mellitus(T2DM)in Malaysia continue to have substantial comorbidities and struggle to achieve glycemic targets.AIM To comprehensively evaluate diabetes self-care and glycemic cont...BACKGROUND Adults with type 2 diabetes mellitus(T2DM)in Malaysia continue to have substantial comorbidities and struggle to achieve glycemic targets.AIM To comprehensively evaluate diabetes self-care and glycemic control using multiple self-reporting questionnaires.METHODS Adults diagnosed with T2DM attending the Seremban Health Clinic were recruited in this cross-sectional study.Eligible participants were recruited based on a consecutive sampling technique,first-come-first-served-basis if they fulfilled the inclusion and exclusion criteria.In addition to the usual sociodemographic,clinical,and laboratory data,the participants answered seven specific self-reporting questionnaires.This report was focused on six key variables:Glycemic control;self-care;self-efficacy;diabetes knowledge;health literacy;and medication adherence.RESULTS A total of 100 adults with T2DM participated.The proportions of participants achieving specific thresholds in the key variables were:Acceptable glycemic control 39.4%;adequate diabetes knowledge 59.6%;sufficient or higher health literacy 80.2%;and medication adherence 51.0%.The mean self-efficacy score was 110.6(73.3%of maximum),and the mean self-care score was 30.7(43.9%of maximum).A statistically significant linear correlation was observed for eight pairs of key variables with Pearson’s correlation values varying between 0.21 to 0.55.Selfefficacy had a relatively higher correlation while glycated hemoglobin was not correlated with other key variables.Path analysis was conducted to examine the relationships among diabetes self-efficacy(Diabetes Management Self Efficacy scale score),self-care behavior(Summary of Diabetes Self-Care Activities score),and glycemic control,but the model demonstrated a poor fit(χ^(2)=28.1,P<0.001).CONCLUSION We found substantial suboptimal glycemic control and low self-care practices but acceptable levels of diabetes knowledge,self-efficacy,health literacy and medication adherence among the patients with T2DM.In spite of the correlations between self-care,self-efficacy,and medication adherence,it was surprising that self-care did not correlate with glycemic control.Prospective cohort studies are needed to explore whether these factors influence diabetes outcomes.展开更多
Traditional medicinal animals and their derivatives hold a significant place within the traditional Chinese medicine framework.However,substantial knowledge about medicinal animals is being lost–particularly within C...Traditional medicinal animals and their derivatives hold a significant place within the traditional Chinese medicine framework.However,substantial knowledge about medicinal animals is being lost–particularly within China’s folk practices and ethnic minority groups–remains unrecorded and unverified scientifically.Such knowledge,primarily preserved through oral instruction,is now at risk of disappearing due to its fragmented and regionalized nature.This paper underscores the importance of documenting and scientifically validating these medicinal animals as valuable resources.We advocate for a comprehensive,systematic approach to recording,screening,and verifying the pharmacological mechanisms of medicinal animals.It can contribute to the modernization and globalization of traditional Chinese medicine.In the future,interdisciplinary and international collaborations are essential to advance the systematic documentation and scientific management of medicinal animal knowledge,to ensure its preservation and application in global healthcare,sustainable health practices,and biodiversity conservation efforts.展开更多
文摘Purpose:This paper compares the paradigmatic differences between knowledge organization(KO)in library and information science and knowledge representation(KR)in AI to show the convergence in KO and KR methods and applications.Methodology:The literature review and comparative analysis of KO and KR paradigms is the primary method used in this paper.Findings:A key difference between KO and KR lays in the purpose of KO is to organize knowledge into certain structure for standardizing and/or normalizing the vocabulary of concepts and relations,while KR is problem-solving oriented.Differences between KO and KR are discussed based on the goal,methods,and functions.Research limitations:This is only a preliminary research with a case study as proof of concept.Practical implications:The paper articulates on the opportunities in applying KR and other AI methods and techniques to enhance the functions of KO.Originality/value:Ontologies and linked data as the evidence of the convergence of KO and KR paradigms provide theoretical and methodological support to innovate KO in the AI era.
基金Supported by the National Social Science Fund of China(No.18BTQ054)
文摘In the big data environment, the construction of massive S&T literature data resources needs intelligent technical assistance. With a focus on comparing the domestic and foreign knowledge organization systems and their applications, this article analyzes and summarizes the gaps in related researches and applications at home and abroad. A knowledge organization system framework for S&T literature data resources is presented in the article. Starting from the basic element of knowledge organization system, it also proposes and designs terminology-based analysis methods and technologies for S&T literature. Based on this framework, it proposes ideas and develops corresponding software tool to carry out relevant experiments. It gives an overview of theories and technologies method for future research.
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.
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZB20230685)the National Science Foundation of China(Grant No.42277161).
文摘Forecasting landslide deformation is challenging due to influence of various internal and external factors on the occurrence of systemic and localized heterogeneities.Despite the potential to improve landslide predictability,deep learning has yet to be sufficiently explored for complex deformation patterns associated with landslides and is inherently opaque.Herein,we developed a holistic landslide deformation forecasting method that considers spatiotemporal correlations of landslide deformation by integrating domain knowledge into interpretable deep learning.By spatially capturing the interconnections between multiple deformations from different observation points,our method contributes to the understanding and forecasting of landslide systematic behavior.By integrating specific domain knowledge relevant to each observation point and merging internal properties with external variables,the local heterogeneity is considered in our method,identifying deformation temporal patterns in different landslide zones.Case studies involving reservoir-induced landslides and creeping landslides demonstrated that our approach(1)enhances the accuracy of landslide deformation forecasting,(2)identifies significant contributing factors and their influence on spatiotemporal deformation characteristics,and(3)demonstrates how identifying these factors and patterns facilitates landslide forecasting.Our research offers a promising and pragmatic pathway toward a deeper understanding and forecasting of complex landslide behaviors.
基金supported by the National Natural Science Foundation of China(Nos.82173746 and U23A20530)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission)。
文摘Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.
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The integration of digital tools and effective knowledge management practices is critical for enhancing administrative efficiency and institutional continuity in higher education. This study investigates the relationships between knowledge modeling, institutional memory, leadership styles, technology, and administrative efficiency at the University of Cape Coast (UCC). The study sought to identify the challenges and opportunities in integrating digital tools into administrative processes and to provide actionable recommendations for improvement. A mixed-methods research design was employed, combining quantitative analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) with qualitative thematic analysis of interviews. The findings revealed key challenges, including resistance to change, fragmented knowledge repositories, and inadequate funding, alongside opportunities such as centralized knowledge systems, cost-effective open-source tools, and capacity-building initiatives. The study highlights the importance of strategic leadership, robust policies, and investments in digital infrastructure to enhance administrative practices. Policy implications include the need for clear digital transformation guidelines and leadership training to foster innovation and collaboration. Recommendations include investing in scalable digital tools, implementing comprehensive capacity-building programs, and promoting stakeholder engagement to drive successful digital integration. These insights provide a roadmap for UCC and similar institutions seeking to optimize administrative efficiency through digital transformation.
文摘This study explores the epistemic imperative to decolonize African education systems by centering indigenous philosophies such as Ubuntu and introducing the Ubuntu Pedagogy as a pedagogical model.Ubuntu pedagogy transforms teacher-learner relationships,it provides a replicable model for relational learning,community partnerships,and reassert the dignity of indigenous epistemologies.The paper examines how language,knowledge production,and pedagogy can be restructured to reflect African epistemologies and educational sovereignty.This research also explores the relationship between mother tongue instruction and cognitive access to learning.Through a qualitative literature analysis of case studies and African scholarly discourse,this paper highlights the continued marginalization of indigenous knowledge systems and the need to embed culturally relevant teaching methodologies.The findings support the broader question of whether there exists an epistemological base for knowledge independence or production within African and Afro-Diasporic contexts,revealing culturally coherent frameworks of learning that resist colonial dominance and an exploration of reclaiming African indigenous knowledge systems for educational and cultural sovereignty.
文摘Indigenous cultures prescribed a means of maximizing the benefits they produced and enjoyed in their relationship with each other and the environment-based on their understanding of the nature of existence and how to live in harmony with the forces shaping the nature of existence.The emergence of civilization introduced the claim that rational abilities superseded indigenous knowledge.This was followed by positivism and the claim that knowledge passed through three stages:mythological,philosophical,and scientific.This impacted indigenous cultures in ways that reached a height when postcolonial development experts convinced national leaders that progress required adopting advances in science.A failure to modernize was regarded as holding back progress.With the development paradigm now regarded as inadequate for achieving its goals and with the rise of the sustainability discourse,there is appreciation for indigenous knowledge.This article describes an indigenous cultural knowledge system that reflects the insight and wisdom of the world’s most respected scientific and philosophical traditions.The beliefs of the Bodo people of Northeast India are used as an example of an indigenous worldview that portrays insight proven to have value that is comparable to the natural sciences,plus theories of natural law and political philosophy.
文摘Objective:This study aimed to explore undergraduates’knowledge,attitude,and practice/behavior of human papillomavirus(HPV)vaccination,as well as the essential influencing factors for vaccination decision-making.Methods:Through cluster and convenience sampling,2000 undergraduates from the Nursing and Language department of a university in Shanghai were sent a self-designed questionnaire.Chi-square tests,independent sample t-test/ANOVE,and multiple linear regression were used to investigate participants’knowledge and attitude on HPV vaccination,as well as the factors that predicted potential action to receive and promote HPV vaccination in the future.Results:The mean HPV knowledge score was 5.027 out of 10.Health science students showed a significantly higher knowledge mean score than the non-health science students(P<0.000).There was a statistically difference in HPV vaccination attitude among undergraduates in different grades(P<0.05).Awareness of cervical cancer and worries about the risk of cervical cancer were the significant predictors of willingness to receive and promote HPV vaccination in the future.Conclusions:It would take time for a new health product to be aware,understood,accepted,and received.Education providing and information sharing are expected to break the dawn and make the procedure processed.
基金Supported by National Key Research and Development Program(Grant No.2024YFB3312700)National Natural Science Foundation of China(Grant No.52405541)the Changzhou Municipal Sci&Tech Program(Grant No.CJ20241131)。
文摘Under the paradigm of Industry 5.0,intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration,where human expertise plays a central role in assembly processes.Despite advancements in intelligent and digital technologies,assembly process design still heavily relies on manual knowledge reuse,and inefficiencies and inconsistent quality in process documentation are caused.To address the aforementioned issues,this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network.First,an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model.Then,a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption.Subsequently,a Bayesian network model is constructed based on the relationships between assembly components,assembly features,and operations.Bayesian network reasoning is used to push assembly process knowledge under different design requirements.Finally,the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example,significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.
文摘The purpose of this study was to examine the knowledge,attitude,motivation and behavior of the community before and after the experiment,and also to determine the effect of the experiment on increasing knowledge,attitude,motivation,and behavior related to the construction of family toilets in coastal areas.The study was conducted in Pangkep and Maros Regencies.Atotal of 50 heads of families were selected as participants using the purposive sampling method.25 participants became the experimental group and 25 people became the control group.The research variables included knowledge,attitudes,motivation,and behavior of the community in building family toilets before and after the experiment.Data collection through tests,questionnaires,and observations to each participant.The research instruments were knowledge tests,questionnaires,and observations.Data analysis used descriptive and inferential statistical analysis,with the t-test.The results of the study showed that based on the experiment,knowledge had a significant effect with a correlation coefficient of 0.94,attitudes had an effect of 0.91,motivation was 0.756,and behavior was 0.865.It can be concluded that the construction of family toilets in the coastal areas of Pangkep and Maros Regencies,before the experiment,the knowledge,attitudes,motivation,and behavior of the community were in the low category,and after the experiment increased significantly to the high category. In addition, the results of the analysis showed that the experiment had a significant effect on increasing theknowledge, attitudes, motivation, and behavior of the community towards the construction of family toilets in coastal areas.
文摘This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.
文摘Background: Blood transfusion (BT) is crucial to the provision of modern health care. However, blood is scarce and costly, and its use is associated with risks. Therefore, the medical professionals who handle it should have adequate knowledge to ensure rational and safe utilization. The objective of the study was to determine the level of BT knowledge among junior medical doctors in Kenya. Methodology: A cross-sectional study was conducted among junior medical doctors working in Western Kenya. Data was collected using questionnaires from August 2021 to March 2022, and analysis was done by way of descriptive and inferential statistics. A p Results: A total of 150 medical doctors participated in the study. Males comprised 60% (n = 90), and the mean age of the participants was 29.9 (SD 3.6) with a range of 25 - 45 years. The mean knowledge score was 54.1% ± 16.4% and was associated with orientation (AOR = 3.157, 95% CI = 1.194 - 8.337). Conclusion: Blood transfusion knowledge among the doctors was suboptimal and was associated with pre-internship induction. There is a need for additional education in BT during all phases of medical training and practice, including orientation for medical interns.
基金Supported by the IMU University Internal Grant,No.CSc-Sem6(12)2022.
文摘BACKGROUND Adults with type 2 diabetes mellitus(T2DM)in Malaysia continue to have substantial comorbidities and struggle to achieve glycemic targets.AIM To comprehensively evaluate diabetes self-care and glycemic control using multiple self-reporting questionnaires.METHODS Adults diagnosed with T2DM attending the Seremban Health Clinic were recruited in this cross-sectional study.Eligible participants were recruited based on a consecutive sampling technique,first-come-first-served-basis if they fulfilled the inclusion and exclusion criteria.In addition to the usual sociodemographic,clinical,and laboratory data,the participants answered seven specific self-reporting questionnaires.This report was focused on six key variables:Glycemic control;self-care;self-efficacy;diabetes knowledge;health literacy;and medication adherence.RESULTS A total of 100 adults with T2DM participated.The proportions of participants achieving specific thresholds in the key variables were:Acceptable glycemic control 39.4%;adequate diabetes knowledge 59.6%;sufficient or higher health literacy 80.2%;and medication adherence 51.0%.The mean self-efficacy score was 110.6(73.3%of maximum),and the mean self-care score was 30.7(43.9%of maximum).A statistically significant linear correlation was observed for eight pairs of key variables with Pearson’s correlation values varying between 0.21 to 0.55.Selfefficacy had a relatively higher correlation while glycated hemoglobin was not correlated with other key variables.Path analysis was conducted to examine the relationships among diabetes self-efficacy(Diabetes Management Self Efficacy scale score),self-care behavior(Summary of Diabetes Self-Care Activities score),and glycemic control,but the model demonstrated a poor fit(χ^(2)=28.1,P<0.001).CONCLUSION We found substantial suboptimal glycemic control and low self-care practices but acceptable levels of diabetes knowledge,self-efficacy,health literacy and medication adherence among the patients with T2DM.In spite of the correlations between self-care,self-efficacy,and medication adherence,it was surprising that self-care did not correlate with glycemic control.Prospective cohort studies are needed to explore whether these factors influence diabetes outcomes.
基金supported by the Project of Innovation team of General Institutes of Higher Education in Guangdong Province(2024KCXTD078)the Project of Integration of resource monitoring,epidemic diseases monitoring and rescue capability of wildlife in 2023(ZT202304111)the Special Project of the Lushan Botanical Garden(No.2024ZWZX06).
文摘Traditional medicinal animals and their derivatives hold a significant place within the traditional Chinese medicine framework.However,substantial knowledge about medicinal animals is being lost–particularly within China’s folk practices and ethnic minority groups–remains unrecorded and unverified scientifically.Such knowledge,primarily preserved through oral instruction,is now at risk of disappearing due to its fragmented and regionalized nature.This paper underscores the importance of documenting and scientifically validating these medicinal animals as valuable resources.We advocate for a comprehensive,systematic approach to recording,screening,and verifying the pharmacological mechanisms of medicinal animals.It can contribute to the modernization and globalization of traditional Chinese medicine.In the future,interdisciplinary and international collaborations are essential to advance the systematic documentation and scientific management of medicinal animal knowledge,to ensure its preservation and application in global healthcare,sustainable health practices,and biodiversity conservation efforts.
