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
Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and...Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and Question-Answering framework powered by an enhanced LLM that integrates a semantic vector database and a curated literature repository.The ERQA framework leverages domain-specific incremental pretraining and conducts supervised fine-tuning on medical literature,enabling retrieval and question-answering(QA)tasks to be completed with high precision.Performance evaluations implemented on the coronavirus disease 2019(COVID-19)and TripClick data-sets demonstrate the robust capabilities of ERQA across multiple tasks.On the COVID-19 dataset,ERQA-13B achieves state-of-the-art retrieval metrics,with normalized discounted cumulative gain at top 10(NDCG@10)0.297,recall values at top 10(Recall@10)0.347,and mean reciprocal rank(MRR)=0.370;it also attains strong abstract summarization performance,with a recall-oriented understudy for gisting evaluation(ROUGE)-1 score of 0.434,and QA performance,with a bilingual evaluation understudy(BLEU)-1 score of 7.851.The comparable performance achieved on the TripClick dataset further under-scores the adaptability of ERQA across diverse medical topics.These findings suggest that ERQA repre-sents a significant step toward efficient biomedical knowledge retrieval and QA.展开更多
This study tested a multilevel model of the workplace territorial behaviors and employees’knowledge sharing relationship,with team identification serving as a mediator and task interdependence as a moderator.Data wer...This study tested a multilevel model of the workplace territorial behaviors and employees’knowledge sharing relationship,with team identification serving as a mediator and task interdependence as a moderator.Data were collected from 253 employees(females=128,mean age=28.626,SD=6.470)from 40 work teams from different industries in China.Path analysis results indicated that workplace territorial behaviors were associated with lower employee knowledge sharing.Team identification enhanced employee knowledge sharing and partially mediated the relationship between workplace territorial behaviors and employee knowledge sharing.Task interdependence enhanced knowledge sharing and strengthened the relationship between team identification and knowledge sharing.Thesefindings extend the proposition of social information processing theory by revealing the mediating role of team identification in the relationship between workplace territorial behaviors and knowledge sharing,and clarifying the boundary conditions of team identification.Practical implications of thesefindings include a need for managers to foster collaborative atmospheres,design interdependent tasks,and mitigate territorial behaviors to enhance team identification and knowledge sharing.展开更多
This text is trying to discuss an approximation to the concept of human emancipation,as part of our well-being,in terms of Education and Knowledge.Without abandoning our metaphysical perception of wholeness,as an exte...This text is trying to discuss an approximation to the concept of human emancipation,as part of our well-being,in terms of Education and Knowledge.Without abandoning our metaphysical perception of wholeness,as an extension of the continuity principle which connects our conscious and unconscious world,emancipation is considered as a personal struggle against all oppressions.Some of these are grounded in our inner world.In accordance with the Enlightenment request,reasoning and knowledge can help us to structure new forms of acceptances which are shaping our own emancipatory meaning.Under the impact of social influence and personal interpretation,the perceived knowledge is considered as a mental tool containing an upgraded valid information.Taking under consideration that this validity is not able to overcome the metaphysical origins of human thought,it is suggested that when this mental tool is functioning in a self-transformative,self-constructed,and flexible form,human intelligence is structuring a compatible information management mechanism,which can enable us to formulate our personal acceptances,bridge our empirical and hyper-empirical inner world,and enlighten our request for self-criticism,self-determination,and above all emancipation.展开更多
BACKGROUND Cerebrovascular disease(CVD)poses a serious threat to human health and safety.Thus,developing a reasonable exercise program plays an important role in the long-term recovery and prognosis for patients with ...BACKGROUND Cerebrovascular disease(CVD)poses a serious threat to human health and safety.Thus,developing a reasonable exercise program plays an important role in the long-term recovery and prognosis for patients with CVD.Studies have shown that predictive nursing can improve the quality of care and that the information–knowledge–attitude–practice(IKAP)nursing model has a positive impact on patients who suffered a stroke.Few studies have combined these two nursing models to treat CVD.AIM To explore the effect of the IKAP nursing model combined with predictive nursing on the Fugl–Meyer motor function(FMA)score,Barthel index score,and disease knowledge mastery rate in patients with CVD.METHODS A total of 140 patients with CVD treated at our hospital between December 2019 and September 2021 were randomly divided into two groups,with 70 patients in each.The control group received routine nursing,while the observation group received the IKAP nursing model combined with predictive nursing.Both groups were observed for self-care ability,motor function,and disease knowledge mastery rate after one month of nursing.RESULTS There was no clear difference between the Barthel index and FMA scores of the two groups before nursing(P>0.05);however,their scores increased after nursing.This increase was more apparent in the observation group,and the difference was statistically significant(P<0.05).The rates of disease knowledge mastery,timely medication,appropriate exercise,and reasonable diet were significantly higher in the observation group than in the control group(P<0.05).The satisfaction rate in the observation group(97.14%)was significantly higher than that in the control group(81.43%;P<0.05).CONCLUSION The IKAP nursing model,combined with predictive nursing,is more effective than routine nursing in the care of patients with CVD,and it can significantly improve the Barthel index and FMA scores with better knowledge acquisition,as well as produce high satisfaction in patients.Moreover,they can be widely used in the clinical setting.展开更多
There are heterogeneous problems between the CAD model and the assembly process document.In the planning stage of assembly process,these heterogeneous problems can decrease the efficiency of information interaction.Ba...There are heterogeneous problems between the CAD model and the assembly process document.In the planning stage of assembly process,these heterogeneous problems can decrease the efficiency of information interaction.Based on knowledge graph,this paper proposes an assembly information model(KGAM)to integrate geometric information from CAD model,non-geometric information and semantic information from assembly process document.KGAM describes the integrated assembly process information as a knowledge graph in the form of“entity-relationship-entity”and“entity-attribute-value”,which can improve the efficiency of information interaction.Taking the trial assembly stage of a certain type of aeroengine compressor rotor component as an example,KGAM is used to get its assembly process knowledge graph.The trial data show the query and update rate of assembly attribute information is improved by more than once.And the query and update rate of assembly semantic information is improved by more than twice.In conclusion,KGAM can solve the heterogeneous problems between the CAD model and the assembly process document and improve the information interaction efficiency.展开更多
The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information...The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
Achieving the Sustainable Development Goal 3.3 is hinged on effective use of information sources for health communication interventions. This study investigated the knowledge of residents of Lagos Nigeria on HIV/HBV c...Achieving the Sustainable Development Goal 3.3 is hinged on effective use of information sources for health communication interventions. This study investigated the knowledge of residents of Lagos Nigeria on HIV/HBV co-infection and the use of information sources. The study adopted the quantitative research method of survey to find out the knowledge level of residents of Lagos, Nigeria on the HIV/HBV co-infection. While the bivariate analysis presented cross tabular data on knowledge level, the multivariate was used to test highlight the hypothesis. The study indicated that more than 75% of the respondents had heard of HIV and HBV co-infection. The result established a significant relationship between the use of information sources and the knowledge on HBV/HIV co-infection. Despite the knowledge on HBV/HIV co-infection, the study concluded on the need for preventive information campaigns to create awareness to mitigate the increasing cases of HBV/HIV co-infection cum motivates individuals toward healthy lifestyle practices.展开更多
Background: Misinformation on interactive Knowledge Exchange Social Websites (KESWs) is concerning since it can influence Internet users’ health behaviors, especially during an infectious disease outbreak. Objective:...Background: Misinformation on interactive Knowledge Exchange Social Websites (KESWs) is concerning since it can influence Internet users’ health behaviors, especially during an infectious disease outbreak. Objective: The present study seeks to examine the accuracy and characteristics of health information posted to a Knowledge Exchange Social Website (KESW). Methods: A sample of 204 answers to Ebola questions were extracted and rated for accuracy. Multiple logistic regression modeling was used to examine whether answer characteristics (best answer, professional background, statistical information, source disclosed, link, and word count) predicted accuracy. Results: Overall, only 27.0% of the posted answers were rated as “accurate”. Accuracy varied across question topics with between 11.8% - 45.5% of answers being rated as accurate. When Yahoo Answers’ “best answers” were examined, the overall accuracy was substantially higher, with 80.0% of “best answers” being rated as accurate compared to 16.0% of all other answers. Conclusion: There is need for tools to help Internet users navigate health information posted on these dynamic user-generated knowledge exchange social websites.展开更多
<strong>Background:</strong> The potential for misinformation on usercontrolled Knowledge Exchange Social Websites (KESWs) is concerning since it can actively influence Internet users’ knowledge, attitude...<strong>Background:</strong> The potential for misinformation on usercontrolled Knowledge Exchange Social Websites (KESWs) is concerning since it can actively influence Internet users’ knowledge, attitudes, and behaviors related to childhood vaccinations. <strong>Objective:</strong> The present study examines the accuracy and predictors of health information posted to a Knowledge Exchange Social Website (KESW). <strong>Methods:</strong> A sample of 480 answers to childhood vaccination questions were retrieved and rated for accuracy. Multiple logistic regression modeling was used to examine whether answer characteristics (best answer, professional background, statistical information, source disclosure, online link, word count, vaccine stance, and tone) predict accuracy. <strong>Results:</strong> Overall, only 56.2% of the posted answers were rated as “accurate.” Accuracy varied by topics with between 52.8% - 64.3% being rated as accurate. When Yahoo Answers’ “best answers” were examined, only 49.2% rated as accurate compared to 57.7% of all other answers, a finding attributed to widespread nominations of vaccine misinformation as “best answers” for questions addressing the side effects of vaccines. For all other types of questions, “best answers” were more likely to be accurate. Regression modeling revealed that discussions of personal choices regarding childhood vaccinations predicted the accuracy of posted answers, with those who mentioned vaccinating their own children proving more likely to communicate accurate vaccine information, and those expressing vaccine hesitancy proving more likely to share factually inaccurate statements about vaccines. <strong>Conclusion:</strong> The high prevalence of misinformation on KESWs suggests that these websites may serve as a vector for spreading vaccine misperceptions. Further research is needed to assess the impact of various KESWs and to develop effective, coordinated responses by public health agencies.展开更多
Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor do...Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,i.e.UKGE-MS.Specifically,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples.Firstly,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature items.Finally,the events knowledge graph is constructed by means of sparse representation and l1 norm.Extensive experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding algorithms.The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery.展开更多
At present,knowledge embedding methods are widely used in the field of knowledge graph(KG)reasoning,and have been successfully applied to those with large entities and relationships.However,in research and production ...At present,knowledge embedding methods are widely used in the field of knowledge graph(KG)reasoning,and have been successfully applied to those with large entities and relationships.However,in research and production environments,there are a large number of KGs with a small number of entities and relations,which are called sparse KGs.Limited by the performance of knowledge extraction methods or some other reasons(some common-sense information does not appear in the natural corpus),the relation between entities is often incomplete.To solve this problem,a method of the graph neural network and information enhancement is proposed.The improved method increases the mean reciprocal rank(MRR)and Hit@3 by 1.6%and 1.7%,respectively,when the sparsity of the FB15K-237 dataset is 10%.When the sparsity is 50%,the evaluation indexes MRR and Hit@10 are increased by 0.8%and 1.8%,respectively.展开更多
This article analyzes the concentration of production and distribution of information, from the analysis of the news in major international news agencies.The biopolitics is more and more interconnected to social, cult...This article analyzes the concentration of production and distribution of information, from the analysis of the news in major international news agencies.The biopolitics is more and more interconnected to social, cultural, economical, and political matters, which led us to see in the contemporary scenario the creation of new forms of social organization which will determine how we interact with each other and how we face the world. Additionally, questions emerge about the usage of the means of communication, particularly those related to Technology of Communication and Information （TICs）. The influence of the media over the social-cultural activities tends to create homogenizations of senses, aiming a planetary visibility in a process that can not only disfigure but destroy many symbolic representations and cultural forms. On the other hand, globalization tends to, instead of minimize the differences in the world, ended up creating new conflicts that, through the usage of new technologies of communication, expresses and articulate themselves.展开更多
Information Technology （IT） consolidates as an essential element to support the business strategies to survive and rapidly adapts to changes in the competitive environment. This paper examines the impact of the use ...Information Technology （IT） consolidates as an essential element to support the business strategies to survive and rapidly adapts to changes in the competitive environment. This paper examines the impact of the use of information systems （IS） and strategic organization knowledge （SOK） on firm performance in 150 Brazilian companies. The study uses partial least squares structural equation modeling （PLS-SEM） and establishes models to express the relationship among the constructs examined. The study identifies that the direct influence of 1S use on performance is moderately significant. However, when mediated by orientation strategy, the total effect of IS use on firm performance is demonstrated to be highly significant. The model explains 54% of the variability of firm performance and confirms IS use as a fundamental resource to support strategic business processes.展开更多
Smart grids have the characteristics of being observable,controllable,adaptive,self-healing,embedded independent processing,and real-time analysis.With the development of smart grids,constructing a grid to cover globa...Smart grids have the characteristics of being observable,controllable,adaptive,self-healing,embedded independent processing,and real-time analysis.With the development of smart grids,constructing a grid to cover global,unified information systems,which should be adapted to fulf ill the requirements of the characteristics,is essential.This paper presents an service-oriented architecture(SOA)for smart grid information-engineering systems based on knowledge grid,which could form as a service-oriented architecture through business,technology and management;it would extract potentially valuable information from the massive amount of information on the generation side,the grid side,and the electricity side,then share the useful information to improve availability,security and stability.展开更多
On the basis of studying general comprehension model of information, this paper puts forward the Four Dimensions Set Information Comprehension Model （FDSICM） based on regarding the new knowledge acquired by cognitiv...On the basis of studying general comprehension model of information, this paper puts forward the Four Dimensions Set Information Comprehension Model （FDSICM） based on regarding the new knowledge acquired by cognitive subject as the fourth dimension set. Making use of the Four Dimension Set Information Comprehension Model （FDSICM）, this paper analyzes the information attributes and expatiates from three levels the comprehension of the information meaning.展开更多
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.
基金supported by the Innovation Fund for Medical Sciences of the Chinese Academy of Medical Sciences(2021-I2M-1-033)the National Key Research and Development Program of China(2022YFF0711900).
文摘Recent advancements in large language models(LLMs)have driven remarkable progress in text process-ing,opening new avenues for medical knowledge discovery.In this study,we present ERQA,a mEdical knowledge Retrieval and Question-Answering framework powered by an enhanced LLM that integrates a semantic vector database and a curated literature repository.The ERQA framework leverages domain-specific incremental pretraining and conducts supervised fine-tuning on medical literature,enabling retrieval and question-answering(QA)tasks to be completed with high precision.Performance evaluations implemented on the coronavirus disease 2019(COVID-19)and TripClick data-sets demonstrate the robust capabilities of ERQA across multiple tasks.On the COVID-19 dataset,ERQA-13B achieves state-of-the-art retrieval metrics,with normalized discounted cumulative gain at top 10(NDCG@10)0.297,recall values at top 10(Recall@10)0.347,and mean reciprocal rank(MRR)=0.370;it also attains strong abstract summarization performance,with a recall-oriented understudy for gisting evaluation(ROUGE)-1 score of 0.434,and QA performance,with a bilingual evaluation understudy(BLEU)-1 score of 7.851.The comparable performance achieved on the TripClick dataset further under-scores the adaptability of ERQA across diverse medical topics.These findings suggest that ERQA repre-sents a significant step toward efficient biomedical knowledge retrieval and QA.
文摘This study tested a multilevel model of the workplace territorial behaviors and employees’knowledge sharing relationship,with team identification serving as a mediator and task interdependence as a moderator.Data were collected from 253 employees(females=128,mean age=28.626,SD=6.470)from 40 work teams from different industries in China.Path analysis results indicated that workplace territorial behaviors were associated with lower employee knowledge sharing.Team identification enhanced employee knowledge sharing and partially mediated the relationship between workplace territorial behaviors and employee knowledge sharing.Task interdependence enhanced knowledge sharing and strengthened the relationship between team identification and knowledge sharing.Thesefindings extend the proposition of social information processing theory by revealing the mediating role of team identification in the relationship between workplace territorial behaviors and knowledge sharing,and clarifying the boundary conditions of team identification.Practical implications of thesefindings include a need for managers to foster collaborative atmospheres,design interdependent tasks,and mitigate territorial behaviors to enhance team identification and knowledge sharing.
文摘This text is trying to discuss an approximation to the concept of human emancipation,as part of our well-being,in terms of Education and Knowledge.Without abandoning our metaphysical perception of wholeness,as an extension of the continuity principle which connects our conscious and unconscious world,emancipation is considered as a personal struggle against all oppressions.Some of these are grounded in our inner world.In accordance with the Enlightenment request,reasoning and knowledge can help us to structure new forms of acceptances which are shaping our own emancipatory meaning.Under the impact of social influence and personal interpretation,the perceived knowledge is considered as a mental tool containing an upgraded valid information.Taking under consideration that this validity is not able to overcome the metaphysical origins of human thought,it is suggested that when this mental tool is functioning in a self-transformative,self-constructed,and flexible form,human intelligence is structuring a compatible information management mechanism,which can enable us to formulate our personal acceptances,bridge our empirical and hyper-empirical inner world,and enlighten our request for self-criticism,self-determination,and above all emancipation.
基金Supported by Basic scientific research industry of Heilongjiang Provincial undergraduate universities in 2019,No.2019-KYYWF-1213.
文摘BACKGROUND Cerebrovascular disease(CVD)poses a serious threat to human health and safety.Thus,developing a reasonable exercise program plays an important role in the long-term recovery and prognosis for patients with CVD.Studies have shown that predictive nursing can improve the quality of care and that the information–knowledge–attitude–practice(IKAP)nursing model has a positive impact on patients who suffered a stroke.Few studies have combined these two nursing models to treat CVD.AIM To explore the effect of the IKAP nursing model combined with predictive nursing on the Fugl–Meyer motor function(FMA)score,Barthel index score,and disease knowledge mastery rate in patients with CVD.METHODS A total of 140 patients with CVD treated at our hospital between December 2019 and September 2021 were randomly divided into two groups,with 70 patients in each.The control group received routine nursing,while the observation group received the IKAP nursing model combined with predictive nursing.Both groups were observed for self-care ability,motor function,and disease knowledge mastery rate after one month of nursing.RESULTS There was no clear difference between the Barthel index and FMA scores of the two groups before nursing(P>0.05);however,their scores increased after nursing.This increase was more apparent in the observation group,and the difference was statistically significant(P<0.05).The rates of disease knowledge mastery,timely medication,appropriate exercise,and reasonable diet were significantly higher in the observation group than in the control group(P<0.05).The satisfaction rate in the observation group(97.14%)was significantly higher than that in the control group(81.43%;P<0.05).CONCLUSION The IKAP nursing model,combined with predictive nursing,is more effective than routine nursing in the care of patients with CVD,and it can significantly improve the Barthel index and FMA scores with better knowledge acquisition,as well as produce high satisfaction in patients.Moreover,they can be widely used in the clinical setting.
基金the National Natural Science Foundation of China(No.51805079)。
文摘There are heterogeneous problems between the CAD model and the assembly process document.In the planning stage of assembly process,these heterogeneous problems can decrease the efficiency of information interaction.Based on knowledge graph,this paper proposes an assembly information model(KGAM)to integrate geometric information from CAD model,non-geometric information and semantic information from assembly process document.KGAM describes the integrated assembly process information as a knowledge graph in the form of“entity-relationship-entity”and“entity-attribute-value”,which can improve the efficiency of information interaction.Taking the trial assembly stage of a certain type of aeroengine compressor rotor component as an example,KGAM is used to get its assembly process knowledge graph.The trial data show the query and update rate of assembly attribute information is improved by more than once.And the query and update rate of assembly semantic information is improved by more than twice.In conclusion,KGAM can solve the heterogeneous problems between the CAD model and the assembly process document and improve the information interaction efficiency.
基金This work is financially supported by the Ministry of Earth Science(MoES),Government of India,(Grant.No.MoES/36/OOIS/Extra/45/2015),URL:https://www.moes.gov.in。
文摘The drastic growth of coastal observation sensors results in copious data that provide weather information.The intricacies in sensor-generated big data are heterogeneity and interpretation,driving high-end Information Retrieval(IR)systems.The Semantic Web(SW)can solve this issue by integrating data into a single platform for information exchange and knowledge retrieval.This paper focuses on exploiting the SWbase systemto provide interoperability through ontologies by combining the data concepts with ontology classes.This paper presents a 4-phase weather data model:data processing,ontology creation,SW processing,and query engine.The developed Oceanographic Weather Ontology helps to enhance data analysis,discovery,IR,and decision making.In addition to that,it also evaluates the developed ontology with other state-of-the-art ontologies.The proposed ontology’s quality has improved by 39.28%in terms of completeness,and structural complexity has decreased by 45.29%,11%and 37.7%in Precision and Accuracy.Indian Meteorological Satellite INSAT-3D’s ocean data is a typical example of testing the proposed model.The experimental result shows the effectiveness of the proposed data model and its advantages in machine understanding and IR.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
文摘Achieving the Sustainable Development Goal 3.3 is hinged on effective use of information sources for health communication interventions. This study investigated the knowledge of residents of Lagos Nigeria on HIV/HBV co-infection and the use of information sources. The study adopted the quantitative research method of survey to find out the knowledge level of residents of Lagos, Nigeria on the HIV/HBV co-infection. While the bivariate analysis presented cross tabular data on knowledge level, the multivariate was used to test highlight the hypothesis. The study indicated that more than 75% of the respondents had heard of HIV and HBV co-infection. The result established a significant relationship between the use of information sources and the knowledge on HBV/HIV co-infection. Despite the knowledge on HBV/HIV co-infection, the study concluded on the need for preventive information campaigns to create awareness to mitigate the increasing cases of HBV/HIV co-infection cum motivates individuals toward healthy lifestyle practices.
文摘Background: Misinformation on interactive Knowledge Exchange Social Websites (KESWs) is concerning since it can influence Internet users’ health behaviors, especially during an infectious disease outbreak. Objective: The present study seeks to examine the accuracy and characteristics of health information posted to a Knowledge Exchange Social Website (KESW). Methods: A sample of 204 answers to Ebola questions were extracted and rated for accuracy. Multiple logistic regression modeling was used to examine whether answer characteristics (best answer, professional background, statistical information, source disclosed, link, and word count) predicted accuracy. Results: Overall, only 27.0% of the posted answers were rated as “accurate”. Accuracy varied across question topics with between 11.8% - 45.5% of answers being rated as accurate. When Yahoo Answers’ “best answers” were examined, the overall accuracy was substantially higher, with 80.0% of “best answers” being rated as accurate compared to 16.0% of all other answers. Conclusion: There is need for tools to help Internet users navigate health information posted on these dynamic user-generated knowledge exchange social websites.
文摘<strong>Background:</strong> The potential for misinformation on usercontrolled Knowledge Exchange Social Websites (KESWs) is concerning since it can actively influence Internet users’ knowledge, attitudes, and behaviors related to childhood vaccinations. <strong>Objective:</strong> The present study examines the accuracy and predictors of health information posted to a Knowledge Exchange Social Website (KESW). <strong>Methods:</strong> A sample of 480 answers to childhood vaccination questions were retrieved and rated for accuracy. Multiple logistic regression modeling was used to examine whether answer characteristics (best answer, professional background, statistical information, source disclosure, online link, word count, vaccine stance, and tone) predict accuracy. <strong>Results:</strong> Overall, only 56.2% of the posted answers were rated as “accurate.” Accuracy varied by topics with between 52.8% - 64.3% being rated as accurate. When Yahoo Answers’ “best answers” were examined, only 49.2% rated as accurate compared to 57.7% of all other answers, a finding attributed to widespread nominations of vaccine misinformation as “best answers” for questions addressing the side effects of vaccines. For all other types of questions, “best answers” were more likely to be accurate. Regression modeling revealed that discussions of personal choices regarding childhood vaccinations predicted the accuracy of posted answers, with those who mentioned vaccinating their own children proving more likely to communicate accurate vaccine information, and those expressing vaccine hesitancy proving more likely to share factually inaccurate statements about vaccines. <strong>Conclusion:</strong> The high prevalence of misinformation on KESWs suggests that these websites may serve as a vector for spreading vaccine misperceptions. Further research is needed to assess the impact of various KESWs and to develop effective, coordinated responses by public health agencies.
基金This study was funded by the International Science and Technology Cooperation Program of the Science and Technology Department of Shaanxi Province,China(No.2021KW-16)the Science and Technology Project in Xi’an(No.2019218114GXRC017CG018-GXYD17.11),Thesis work was supported by the special fund construction project of Key Disciplines in Ordinary Colleges and Universities in Shaanxi Province,the authors would like to thank the anonymous reviewers for their helpful comments and suggestions.
文摘Text event mining,as an indispensable method of text mining processing,has attracted the extensive attention of researchers.A modeling method for knowledge graph of events based on mutual information among neighbor domains and sparse representation is proposed in this paper,i.e.UKGE-MS.Specifically,UKGE-MS can improve the existing text mining technology's ability of understanding and discovering high-dimensional unmarked information,and solves the problems of traditional unsupervised feature selection methods,which only focus on selecting features from a global perspective and ignoring the impact of local connection of samples.Firstly,considering the influence of local information of samples in feature correlation evaluation,a feature clustering algorithm based on average neighborhood mutual information is proposed,and the feature clusters with certain event correlation are obtained;Secondly,an unsupervised feature selection method based on the high-order correlation of multi-dimensional statistical data is designed by combining the dimension reduction advantage of local linear embedding algorithm and the feature selection ability of sparse representation,so as to enhance the generalization ability of the selected feature items.Finally,the events knowledge graph is constructed by means of sparse representation and l1 norm.Extensive experiments are carried out on five real datasets and synthetic datasets,and the UKGE-MS are compared with five corresponding algorithms.The experimental results show that UKGE-MS is better than the traditional method in event clustering and feature selection,and has some advantages over other methods in text event recognition and discovery.
基金supported by the Sichuan Science and Technology Program under Grants No.2022YFQ0052 and No.2021YFQ0009.
文摘At present,knowledge embedding methods are widely used in the field of knowledge graph(KG)reasoning,and have been successfully applied to those with large entities and relationships.However,in research and production environments,there are a large number of KGs with a small number of entities and relations,which are called sparse KGs.Limited by the performance of knowledge extraction methods or some other reasons(some common-sense information does not appear in the natural corpus),the relation between entities is often incomplete.To solve this problem,a method of the graph neural network and information enhancement is proposed.The improved method increases the mean reciprocal rank(MRR)and Hit@3 by 1.6%and 1.7%,respectively,when the sparsity of the FB15K-237 dataset is 10%.When the sparsity is 50%,the evaluation indexes MRR and Hit@10 are increased by 0.8%and 1.8%,respectively.
文摘This article analyzes the concentration of production and distribution of information, from the analysis of the news in major international news agencies.The biopolitics is more and more interconnected to social, cultural, economical, and political matters, which led us to see in the contemporary scenario the creation of new forms of social organization which will determine how we interact with each other and how we face the world. Additionally, questions emerge about the usage of the means of communication, particularly those related to Technology of Communication and Information （TICs）. The influence of the media over the social-cultural activities tends to create homogenizations of senses, aiming a planetary visibility in a process that can not only disfigure but destroy many symbolic representations and cultural forms. On the other hand, globalization tends to, instead of minimize the differences in the world, ended up creating new conflicts that, through the usage of new technologies of communication, expresses and articulate themselves.
文摘Information Technology （IT） consolidates as an essential element to support the business strategies to survive and rapidly adapts to changes in the competitive environment. This paper examines the impact of the use of information systems （IS） and strategic organization knowledge （SOK） on firm performance in 150 Brazilian companies. The study uses partial least squares structural equation modeling （PLS-SEM） and establishes models to express the relationship among the constructs examined. The study identifies that the direct influence of 1S use on performance is moderately significant. However, when mediated by orientation strategy, the total effect of IS use on firm performance is demonstrated to be highly significant. The model explains 54% of the variability of firm performance and confirms IS use as a fundamental resource to support strategic business processes.
文摘Smart grids have the characteristics of being observable,controllable,adaptive,self-healing,embedded independent processing,and real-time analysis.With the development of smart grids,constructing a grid to cover global,unified information systems,which should be adapted to fulf ill the requirements of the characteristics,is essential.This paper presents an service-oriented architecture(SOA)for smart grid information-engineering systems based on knowledge grid,which could form as a service-oriented architecture through business,technology and management;it would extract potentially valuable information from the massive amount of information on the generation side,the grid side,and the electricity side,then share the useful information to improve availability,security and stability.
文摘On the basis of studying general comprehension model of information, this paper puts forward the Four Dimensions Set Information Comprehension Model （FDSICM） based on regarding the new knowledge acquired by cognitive subject as the fourth dimension set. Making use of the Four Dimension Set Information Comprehension Model （FDSICM）, this paper analyzes the information attributes and expatiates from three levels the comprehension of the information meaning.
