After the first secondarily-transmitted ebola case in Spain, a wave of divergent opinions flooded mass and sanitary media. Very few of these opinions, however, came from experts on epidemiology or hemorrhagic fevers. ...After the first secondarily-transmitted ebola case in Spain, a wave of divergent opinions flooded mass and sanitary media. Very few of these opinions, however, came from experts on epidemiology or hemorrhagic fevers. This observational study aimed to assess the specific knowledge of Primary Care physicians and nurses about ebola and hemorrhagic fevers by means of analyzing the results obtained from a 5-item self-reported questionnaire dealing on hemorrhagic fevers basic knowledge. Validity and reliability of questionnaire were confirmed by a pilot study. The participants were 138 family doctors and nurses from the 64 public Primary Care centers sited in the North Metropolitan Area of Barcelona (1,400,000 inhab;Catalonia, Spain) taking part in training-the-trainers ebola workshops. Overall, there were 117 (84.8%) respondents out from 138 workshop participants;of them were physicians 61 (51.2%). The main age was 46.7 (8.8) years;stating previous specific knowledge on hemorrhagic fevers 39 (33.3%). On the whole, up to 92 (78.6%) of respondents shown a poor knowledge. Previous specific formation was significantly and independently associated with having proper knowledge (p < 0.001);OR = 8.6 (CI 95%: 3.199 - 23.623). In summary, confusion that accompanied the single secondary-transmitted ebola case in Spain could probably be explained by the existence of a serious gap on hemorrhagic fevers knowledge. More accurate, scientific and formally-presented information should be provided to Primary Care physicians and nurses.展开更多
Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representati...Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.展开更多
Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations a...Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.展开更多
The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on e...The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on empirical models with limited predictive capabilities.This study focuses on the influence of optical basicity on viscosity in CaO-Al_(2)O_(3)-based refining slags,leveraging machine learning to address data scarcity and improve prediction accuracy.An automated framework for algorithm integration,parameter tuning,and evaluation ranking framework(Auto-APE)is employed to develop customized data-driven models for various slag systems,including CaO-Al_(2)O_(3)-SiO_(2),CaO-Al_(2)O_(3)-CaF_(2),CaO-Al_(2)O_(3)-SiO_(2)-MgO,and CaO-Al_(2)O_(3)-SiO_(2)-MgO-CaF_(2).By incorporating optical basicity as a key feature,the models achieve an average validation error of 8.0%to 15.1%,significantly outperforming traditional empirical models.Additionally,symbolic regression is introduced to rapidly construct domain-specific features,such as optical basicity-like descriptors,offering a potential breakthrough in performance prediction for small datasets.This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity,providing a robust machine learning-based approach for optimizing refining slag properties.展开更多
With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or p...With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.展开更多
Amidst evolving user behavior driven by the development of the internet,enhancing the operational quality of trade publishing knowledge service platforms has become a significant challenge for publishing institutions....Amidst evolving user behavior driven by the development of the internet,enhancing the operational quality of trade publishing knowledge service platforms has become a significant challenge for publishing institutions.To address this issue,this paper employs a combined approach of theoretical analysis and case study,introducing the SICAS(Sense-Interest-Connection-Action-Share)user consumption behavior analysis model and selecting“CITIC Academy”as the case study subject.It systematically examines and summarizes the platform’s operational practices and specific strategies,aiming to offer strategic insights and practical references for the operational improvement and sustainable,high-quality development of trade publishing knowledge service platforms.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
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
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
Water scarcity poses a significant challenge globally,with South Africa exemplifying the severe socio-economic and environmental impacts of limited water access.Despite advances in modern water management systems,the ...Water scarcity poses a significant challenge globally,with South Africa exemplifying the severe socio-economic and environmental impacts of limited water access.Despite advances in modern water management systems,the integration of indigenous knowledge(IK)into formal frameworks remains underutilized.This study systematically reviews the role of indigenous water conservation practices in South Africa,analyzing over 50 high-quality sources using the PRISMA methodology.The findings highlight the effectiveness of IK in addressing water scarcity through techniques such as rainwater harvesting,terracing,and wetland management,which are low-cost,environmentally sustainable,and deeply rooted in cultural practices.Indigenous methods also enhance climate resilience by enabling communities to adapt to droughts and floods through practices such as weather prediction and adaptive farming techniques.Furthermore,these practices foster social inclusivity and community empowerment,ensuring equitable water access and intergenerational knowledge transfer.The study underscores the potential of integrating IK with modern water technologies to create holistic solutions that are scalable,sustainable,and aligned with South Africa’s goal of achieving water security by 2030.Policy recommendations emphasize the need for institutional support,data collection,and financial incentives to sustain and mainstream indigenous approaches.By bridging the gap between traditional and contemporary systems,this research provides a roadmap for leveraging diverse knowledge systems to address water scarcity and build resilient communities.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
Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpret...Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpretability.A promising approach to overcoming these challenges is to embed domain knowledge into the ML pipeline,enhancing the model with additional pattern information.In this paper,we review the latest developments in PHM,encapsulated under the concept of Knowledge Driven Machine Learning(KDML).We propose a hierarchical framework to define KDML in PHM,which includes scientific paradigms,knowledge sources,knowledge representations,and knowledge embedding methods.Using this framework,we examine current research to demonstrate how various forms of knowledge can be integrated into the ML pipeline and provide roadmap to specific usage.Furthermore,we present several case studies that illustrate specific implementations of KDML in the PHM domain,including inductive experience,physical model,and signal processing.We analyze the improvements in generalization capability and interpretability that KDML can achieve.Finally,we discuss the challenges,potential applications,and usage recommendations of KDML in PHM,with a particular focus on the critical need for interpretability to ensure trustworthy deployment of artificial intelligence in PHM.展开更多
In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilizati...In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.展开更多
Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,stressed that we should adhere to the“two integrations”(namely,integrating the basic tenets of Marxism with China’s specific realit...Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,stressed that we should adhere to the“two integrations”(namely,integrating the basic tenets of Marxism with China’s specific realities and fine traditional culture),root ourselves in Chinese soil,carry forward the Chinese cultural heritage,and strengthen the academic foundation.We should accelerate the building of an independent knowledge system for Chinese philosophy and social sciences,and formulate original concepts and develop systems of academic discipline,research and discourse,drawing on China’s rich experience of advancing human rights.In the face of changes of a magnitude not seen in a century,in the historic process of advancing the great rejuvenation of the Chinese nation on all fronts through Chinese modernization,we should and must strengthen our theoretical self-consciousness and confidence in the path of Chinese modernization.We need to enhance human rights research,develop the human rights theoretical system and paradigm that are based on Chinese realities and express Chinese voice,and an independent Chinese knowledge system for human rights.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
Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple modalities.Despite these achievements,LLMs have in...Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple modalities.Despite these achievements,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific accuracy.To address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model co-evolution.First,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge prompting.Second,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge injection.Third,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual learning.We illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various domains.We conclude by outlining future pathways for further advancement and applications.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
BACKGROUND Ischemic bowel disease(IBD)is a critical condition caused by reduced blood flow to the intestines,leading to tissue damage and potentially severe complications.Early recognition and timely management are es...BACKGROUND Ischemic bowel disease(IBD)is a critical condition caused by reduced blood flow to the intestines,leading to tissue damage and potentially severe complications.Early recognition and timely management are essential for improving patient outcomes and reducing morbidity and mortality associated with IBD.AIM To evaluate the knowledge,attitude and practice(KAP)of healthcare professionals regarding IBD.METHODS This cross-sectional study was conducted among healthcare professionals in China from November 2023 to December 2023 using a self-designed questionnaire.RESULTS A total of 315 valid questionnaires were analyzed,with 215 participants(68.25%)being female.The mean KAP scores were 17.55±5.35(range:0-24),27.65±2.77(range:8-40),and 18.88±4.23(range:6-30),respectively.Multivariate linear regression analysis revealed the following factors to be independently associated with knowledge:Age 26-35 years(β=2.80,95%CI:0.31-5.30,P=0.028),professional title(β=2.66,95%CI:0.91-4.41,P=0.003),position(β=-3.78,95%CI:-5.45 to-2.11,P<0.001),participation in IBD-related training(β=3.45,95%CI:2.39-4.51,P<0.001),and admission of more than five IBD cases in the past month(β=3.25,95%CI:1.58-4.92,P<0.001).Attitude was independently associated with knowledge(β=0.20,95%CI:0.15-0.26,P<0.001)and being a nurse or nursing supervisor(β=-1.30,95%CI:-2.16 to-0.40,P=0.003).Practice was independently associated with knowledge(β=0.20,95%CI:0.10-0.30,P<0.001)and attitude(β=0.24,95%CI:0.06-0.42,P=0.007).Structural equation modeling demonstrated direct effects of knowledge on attitude(β=0.24,P<0.001)and practice(β=0.26,P<0.001),as well as of attitude on practice(β=0.22,P=0.012).CONCLUSION Healthcare professionals demonstrated adequate knowledge but moderate attitude and inactive practice regarding IBD.Addressing the gaps in attitude and practice through targeted training programs and interventions is essential for improving patient care and outcomes.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
Tracking and analyzing data from research projects is critical for understanding research trends and supporting the development of science and technology strategies.However,the data from these projects is often comple...Tracking and analyzing data from research projects is critical for understanding research trends and supporting the development of science and technology strategies.However,the data from these projects is often complex and inadequate,making it challenging for researchers to conduct in-depth data mining to improve policies or management.To address this problem,this paper adopts a top-down approach to construct a knowledge graph(KG)for research projects.Firstly,we construct an integrated ontology by referring to the metamodel of various architectures,which is called the meta-model integration conceptual reference model.Subsequently,we use the dependency parsing method to extract knowledge from unstructured textual data and use the entity alignment method based on weakly supervised learning to classify the extracted entities,completing the construction of the KG for the research projects.In addition,a knowledge inference model based on representation learning is employed to achieve knowledge completion and improve the KG.Finally,experiments are conducted on the KG for research projects and the results demonstrate the effectiveness of the proposed method in enriching incomplete data within the KG.展开更多
文摘After the first secondarily-transmitted ebola case in Spain, a wave of divergent opinions flooded mass and sanitary media. Very few of these opinions, however, came from experts on epidemiology or hemorrhagic fevers. This observational study aimed to assess the specific knowledge of Primary Care physicians and nurses about ebola and hemorrhagic fevers by means of analyzing the results obtained from a 5-item self-reported questionnaire dealing on hemorrhagic fevers basic knowledge. Validity and reliability of questionnaire were confirmed by a pilot study. The participants were 138 family doctors and nurses from the 64 public Primary Care centers sited in the North Metropolitan Area of Barcelona (1,400,000 inhab;Catalonia, Spain) taking part in training-the-trainers ebola workshops. Overall, there were 117 (84.8%) respondents out from 138 workshop participants;of them were physicians 61 (51.2%). The main age was 46.7 (8.8) years;stating previous specific knowledge on hemorrhagic fevers 39 (33.3%). On the whole, up to 92 (78.6%) of respondents shown a poor knowledge. Previous specific formation was significantly and independently associated with having proper knowledge (p < 0.001);OR = 8.6 (CI 95%: 3.199 - 23.623). In summary, confusion that accompanied the single secondary-transmitted ebola case in Spain could probably be explained by the existence of a serious gap on hemorrhagic fevers knowledge. More accurate, scientific and formally-presented information should be provided to Primary Care physicians and nurses.
文摘Since Google introduced the concept of Knowledge Graphs(KGs)in 2012,their construction technologies have evolved into a comprehensive methodological framework encompassing knowledge acquisition,extraction,representation,modeling,fusion,computation,and storage.Within this framework,knowledge extraction,as the core component,directly determines KG quality.In military domains,traditional manual curation models face efficiency constraints due to data fragmentation,complex knowledge architectures,and confidentiality protocols.Meanwhile,crowdsourced ontology construction approaches from general domains prove non-transferable,while human-crafted ontologies struggle with generalization deficiencies.To address these challenges,this study proposes an OntologyAware LLM Methodology for Military Domain Knowledge Extraction(LLM-KE).This approach leverages the deep semantic comprehension capabilities of Large Language Models(LLMs)to simulate human experts’cognitive processes in crowdsourced ontology construction,enabling automated extraction of military textual knowledge.It concurrently enhances knowledge processing efficiency and improves KG completeness.Empirical analysis demonstrates that this method effectively resolves scalability and dynamic adaptation challenges in military KG construction,establishing a novel technological pathway for advancing military intelligence development.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3209504)Natural Science Foundation of Wuhan(Grant No.2024040801020271)the Fundamental Research Funds for Central Public Welfare Research Institutes(Grant No.CKSF2025718/YT).
文摘Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.
基金supported by the National Key Research and Development Program of China(No.2023YFB3712401),the National Natural Science Foundation of China(No.52274301)the Aeronautical Science Foundation of China(No.2023Z0530S6005)the Ningbo Yongjiang Talent-Introduction Programme(No.2022A-023-C).
文摘The viscosity of refining slags plays a critical role in metallurgical processes.However,obtaining accurate viscosity data remains challenging due to the complexities of high-temperature experiments,often relying on empirical models with limited predictive capabilities.This study focuses on the influence of optical basicity on viscosity in CaO-Al_(2)O_(3)-based refining slags,leveraging machine learning to address data scarcity and improve prediction accuracy.An automated framework for algorithm integration,parameter tuning,and evaluation ranking framework(Auto-APE)is employed to develop customized data-driven models for various slag systems,including CaO-Al_(2)O_(3)-SiO_(2),CaO-Al_(2)O_(3)-CaF_(2),CaO-Al_(2)O_(3)-SiO_(2)-MgO,and CaO-Al_(2)O_(3)-SiO_(2)-MgO-CaF_(2).By incorporating optical basicity as a key feature,the models achieve an average validation error of 8.0%to 15.1%,significantly outperforming traditional empirical models.Additionally,symbolic regression is introduced to rapidly construct domain-specific features,such as optical basicity-like descriptors,offering a potential breakthrough in performance prediction for small datasets.This work highlights the critical role of domain-specific knowledge in understanding and predicting viscosity,providing a robust machine learning-based approach for optimizing refining slag properties.
基金funded by the Hunan Provincial Natural Science Foundation of China(Grant No.2025JJ70105)the Hunan Provincial College Students’Innovation and Entrepreneurship Training Program(Project No.S202411342056)The article processing charge(APC)was funded by the Project No.2025JJ70105.
文摘With the widespread use of social media,the propagation of health-related rumors has become a significant public health threat.Existing methods for detecting health rumors predominantly rely on external knowledge or propagation structures,with only a few recent approaches attempting causal inference;however,these have not yet effectively integrated causal discovery with domain-specific knowledge graphs for detecting health rumors.In this study,we found that the combined use of causal discovery and domain-specific knowledge graphs can effectively identify implicit pseudo-causal logic embedded within texts,holding significant potential for health rumor detection.To this end,we propose CKDG—a dual-graph fusion framework based on causal logic and medical knowledge graphs.CKDG constructs a weighted causal graph to capture the implicit causal relationships in the text and introduces a medical knowledge graph to verify semantic consistency,thereby enhancing the ability to identify the misuse of professional terminology and pseudoscientific claims.In experiments conducted on a dataset comprising 8430 health rumors,CKDG achieved an accuracy of 91.28%and an F1 score of 90.38%,representing improvements of 5.11%and 3.29%over the best baseline,respectively.Our results indicate that the integrated use of causal discovery and domainspecific knowledge graphs offers significant advantages for health rumor detection systems.This method not only improves detection performance but also enhances the transparency and credibility of model decisions by tracing causal chains and sources of knowledge conflicts.We anticipate that this work will provide key technological support for the development of trustworthy health-information filtering systems,thereby improving the reliability of public health information on social media.
文摘Amidst evolving user behavior driven by the development of the internet,enhancing the operational quality of trade publishing knowledge service platforms has become a significant challenge for publishing institutions.To address this issue,this paper employs a combined approach of theoretical analysis and case study,introducing the SICAS(Sense-Interest-Connection-Action-Share)user consumption behavior analysis model and selecting“CITIC Academy”as the case study subject.It systematically examines and summarizes the platform’s operational practices and specific strategies,aiming to offer strategic insights and practical references for the operational improvement and sustainable,high-quality development of trade publishing knowledge service platforms.
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.
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.
文摘Water scarcity poses a significant challenge globally,with South Africa exemplifying the severe socio-economic and environmental impacts of limited water access.Despite advances in modern water management systems,the integration of indigenous knowledge(IK)into formal frameworks remains underutilized.This study systematically reviews the role of indigenous water conservation practices in South Africa,analyzing over 50 high-quality sources using the PRISMA methodology.The findings highlight the effectiveness of IK in addressing water scarcity through techniques such as rainwater harvesting,terracing,and wetland management,which are low-cost,environmentally sustainable,and deeply rooted in cultural practices.Indigenous methods also enhance climate resilience by enabling communities to adapt to droughts and floods through practices such as weather prediction and adaptive farming techniques.Furthermore,these practices foster social inclusivity and community empowerment,ensuring equitable water access and intergenerational knowledge transfer.The study underscores the potential of integrating IK with modern water technologies to create holistic solutions that are scalable,sustainable,and aligned with South Africa’s goal of achieving water security by 2030.Policy recommendations emphasize the need for institutional support,data collection,and financial incentives to sustain and mainstream indigenous approaches.By bridging the gap between traditional and contemporary systems,this research provides a roadmap for leveraging diverse knowledge systems to address water scarcity and build resilient communities.
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 in part by Science Center for Gas Turbine Project(Project No.P2022-DC-I-003-001)National Natural Science Foundation of China(Grant No.52275130).
文摘Despite significant progress in the Prognostics and Health Management(PHM)domain using pattern learning systems from data,machine learning(ML)still faces challenges related to limited generalization and weak interpretability.A promising approach to overcoming these challenges is to embed domain knowledge into the ML pipeline,enhancing the model with additional pattern information.In this paper,we review the latest developments in PHM,encapsulated under the concept of Knowledge Driven Machine Learning(KDML).We propose a hierarchical framework to define KDML in PHM,which includes scientific paradigms,knowledge sources,knowledge representations,and knowledge embedding methods.Using this framework,we examine current research to demonstrate how various forms of knowledge can be integrated into the ML pipeline and provide roadmap to specific usage.Furthermore,we present several case studies that illustrate specific implementations of KDML in the PHM domain,including inductive experience,physical model,and signal processing.We analyze the improvements in generalization capability and interpretability that KDML can achieve.Finally,we discuss the challenges,potential applications,and usage recommendations of KDML in PHM,with a particular focus on the critical need for interpretability to ensure trustworthy deployment of artificial intelligence in PHM.
文摘In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries.
文摘Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,stressed that we should adhere to the“two integrations”(namely,integrating the basic tenets of Marxism with China’s specific realities and fine traditional culture),root ourselves in Chinese soil,carry forward the Chinese cultural heritage,and strengthen the academic foundation.We should accelerate the building of an independent knowledge system for Chinese philosophy and social sciences,and formulate original concepts and develop systems of academic discipline,research and discourse,drawing on China’s rich experience of advancing human rights.In the face of changes of a magnitude not seen in a century,in the historic process of advancing the great rejuvenation of the Chinese nation on all fronts through Chinese modernization,we should and must strengthen our theoretical self-consciousness and confidence in the path of Chinese modernization.We need to enhance human rights research,develop the human rights theoretical system and paradigm that are based on Chinese realities and express Chinese voice,and an independent Chinese knowledge system for human rights.
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 in part by National Natural Science Foundation of China(62441605)。
文摘Large language models(LLMs)have significantly advanced artificial intelligence(AI)by excelling in tasks such as understanding,generation,and reasoning across multiple modalities.Despite these achievements,LLMs have inherent limitations including outdated information,hallucinations,inefficiency,lack of interpretability,and challenges in domain-specific accuracy.To address these issues,this survey explores three promising directions in the post-LLM era:knowledge empowerment,model collaboration,and model co-evolution.First,we examine methods of integrating external knowledge into LLMs to enhance factual accuracy,reasoning capabilities,and interpretability,including incorporating knowledge into training objectives,instruction tuning,retrieval-augmented inference,and knowledge prompting.Second,we discuss model collaboration strategies that leverage the complementary strengths of LLMs and smaller models to improve efficiency and domain-specific performance through techniques such as model merging,functional model collaboration,and knowledge injection.Third,we delve into model co-evolution,in which multiple models collaboratively evolve by sharing knowledge,parameters,and learning strategies to adapt to dynamic environments and tasks,thereby enhancing their adaptability and continual learning.We illustrate how the integration of these techniques advances AI capabilities in science,engineering,and society—particularly in hypothesis development,problem formulation,problem-solving,and interpretability across various domains.We conclude by outlining future pathways for further advancement and applications.
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BACKGROUND Ischemic bowel disease(IBD)is a critical condition caused by reduced blood flow to the intestines,leading to tissue damage and potentially severe complications.Early recognition and timely management are essential for improving patient outcomes and reducing morbidity and mortality associated with IBD.AIM To evaluate the knowledge,attitude and practice(KAP)of healthcare professionals regarding IBD.METHODS This cross-sectional study was conducted among healthcare professionals in China from November 2023 to December 2023 using a self-designed questionnaire.RESULTS A total of 315 valid questionnaires were analyzed,with 215 participants(68.25%)being female.The mean KAP scores were 17.55±5.35(range:0-24),27.65±2.77(range:8-40),and 18.88±4.23(range:6-30),respectively.Multivariate linear regression analysis revealed the following factors to be independently associated with knowledge:Age 26-35 years(β=2.80,95%CI:0.31-5.30,P=0.028),professional title(β=2.66,95%CI:0.91-4.41,P=0.003),position(β=-3.78,95%CI:-5.45 to-2.11,P<0.001),participation in IBD-related training(β=3.45,95%CI:2.39-4.51,P<0.001),and admission of more than five IBD cases in the past month(β=3.25,95%CI:1.58-4.92,P<0.001).Attitude was independently associated with knowledge(β=0.20,95%CI:0.15-0.26,P<0.001)and being a nurse or nursing supervisor(β=-1.30,95%CI:-2.16 to-0.40,P=0.003).Practice was independently associated with knowledge(β=0.20,95%CI:0.10-0.30,P<0.001)and attitude(β=0.24,95%CI:0.06-0.42,P=0.007).Structural equation modeling demonstrated direct effects of knowledge on attitude(β=0.24,P<0.001)and practice(β=0.26,P<0.001),as well as of attitude on practice(β=0.22,P=0.012).CONCLUSION Healthcare professionals demonstrated adequate knowledge but moderate attitude and inactive practice regarding IBD.Addressing the gaps in attitude and practice through targeted training programs and interventions is essential for improving patient care and outcomes.
基金Deep-time Digital Earth(DDE)Big Science Program(No.GJ-C03-SGF-2025-004)National Natural Science Foundation of China(No.42394063)Sichuan Science and Technology Program(No.2025ZNSFSC0325).
文摘Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.
基金supported by the National Natural Science Foundation of China(72101263).
文摘Tracking and analyzing data from research projects is critical for understanding research trends and supporting the development of science and technology strategies.However,the data from these projects is often complex and inadequate,making it challenging for researchers to conduct in-depth data mining to improve policies or management.To address this problem,this paper adopts a top-down approach to construct a knowledge graph(KG)for research projects.Firstly,we construct an integrated ontology by referring to the metamodel of various architectures,which is called the meta-model integration conceptual reference model.Subsequently,we use the dependency parsing method to extract knowledge from unstructured textual data and use the entity alignment method based on weakly supervised learning to classify the extracted entities,completing the construction of the KG for the research projects.In addition,a knowledge inference model based on representation learning is employed to achieve knowledge completion and improve the KG.Finally,experiments are conducted on the KG for research projects and the results demonstrate the effectiveness of the proposed method in enriching incomplete data within the KG.
