GATiT:An Intelligent Diagnosis Model Based on Graph Attention Network Incorporating Text Representation in Knowledge Reasoning

The growing prevalence of knowledge reasoning using knowledge graphs(KGs)has substantially improved the accuracy and efficiency of intelligent medical diagnosis.However,current models primarily integrate electronic medical records(EMRs)and KGs into the knowledge reasoning process,ignoring the differing significance of various types of knowledge in EMRs and the diverse data types present in the text.To better integrate EMR text information,we propose a novel intelligent diagnostic model named the Graph ATtention network incorporating Text representation in knowledge reasoning(GATiT),which comprises text representation,subgraph construction,knowledge reasoning,and diagnostic classification.In the text representation process,GATiT uses a pre-trained model to obtain text representations of the EMRs and additionally enhances embeddings by including chief complaint information and numerical information in the input.In the subgraph construction process,GATiT constructs text subgraphs and disease subgraphs from the KG,utilizing EMR text and the disease to be diagnosed.To differentiate the varying importance of nodes within the subgraphs features such as node categories,relevance scores,and other relevant factors are introduced into the text subgraph.Themessage-passing strategy and attention weight calculation of the graph attention network are adjusted to learn these features in the knowledge reasoning process.Finally,in the diagnostic classification process,the interactive attention-based fusion method integrates the results of knowledge reasoning with text representations to produce the final diagnosis results.Experimental results on multi-label and single-label EMR datasets demonstrate the model’s superiority over several state-of-theart methods.

Reflective practice and knowledge development: Transforming research for a practice-based discipline

Knowledge development to guide evidence-informed practice is a cornerstone of nursing as a practice-based discipline.The emphasis on empirical knowledge development overshadows other ways of knowledge developmentdpersonal,aesthetic,and ethical.Technical,objective knowledge development is more dominant than knowledge development for delivering holistic,personcentered care.Personal,aesthetic,and ethical ways of knowing are essential factors in satisfying work environments,patient satisfaction,and nurse retention.Boyer's model of scholarship development defining the scholarship of discovery,teaching,application,and integration guide nurses in building programs of scholarship informing the practice of nursing in practice and academia with an aim of improving and transforming healthcare delivery and patient outcomes.The purpose of this paper is to describe the various forms of scholarship described by Boyer as priorities in knowledge development,examine how the multiple ways of knowing expand traditional empirical perspectives of knowledge development,and present the value of reflective practices that undergird knowledge generation,integration,and application for holistic personcentered safe quality care.Reflective practices have a unique contribution to forming the unique art and science of nursing as a practice-based discipline.

An Intelligent Quality Control Method for Manufacturing Processes Based on a Human–Cyber–Physical Knowledge Graph

Quality management is a constant and significant concern in enterprises.Effective determination of correct solutions for comprehensive problems helps avoid increased backtesting costs.This study proposes an intelligent quality control method for manufacturing processes based on a human–cyber–physical(HCP)knowledge graph,which is a systematic method that encompasses the following elements:data management and classification based on HCP ternary data,HCP ontology construction,knowledge extraction for constructing an HCP knowledge graph,and comprehensive application of quality control based on HCP knowledge.The proposed method implements case retrieval,automatic analysis,and assisted decision making based on an HCP knowledge graph,enabling quality monitoring,inspection,diagnosis,and maintenance strategies for quality control.In practical applications,the proposed modular and hierarchical HCP ontology exhibits significant superiority in terms of shareability and reusability of the acquired knowledge.Moreover,the HCP knowledge graph deeply integrates the provided HCP data and effectively supports comprehensive decision making.The proposed method was implemented in cases involving an automotive production line and a gear manufacturing process,and the effectiveness of the method was verified by the application system deployed.Furthermore,the proposed method can be extended to other manufacturing process quality control tasks.

De-biased knowledge distillation framework based on knowledge infusion and label de-biasing techniques

Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various domains.However,the problem of student model performance being limited due to inherent biases in the teacher model during the distillation process still persists.To address the inherent biases in knowledge distillation,we propose a de-biased knowledge distillation framework tailored for binary classification tasks.For the pre-trained teacher model,biases in the soft labels are mitigated through knowledge infusion and label de-biasing techniques.Based on this,a de-biased distillation loss is introduced,allowing the de-biased labels to replace the soft labels as the fitting target for the student model.This approach enables the student model to learn from the corrected model information,achieving high-performance deployment on lightweight student models.Experiments conducted on multiple real-world datasets demonstrate that deep learning models compressed under the de-biased knowledge distillation framework significantly outperform traditional response-based and feature-based knowledge distillation models across various evaluation metrics,highlighting the effectiveness and superiority of the de-biased knowledge distillation framework in model compression.

Knowledge Reasoning Method Based on Deep Transfer Reinforcement Learning:DTRLpath

In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning.By searching paths on the knowledge graph and making fact and link predictions based on these paths,deep learning-based Reinforcement Learning(RL)agents can demonstrate good performance and interpretability.Therefore,deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic.However,even in a small and fixed knowledge graph reasoning action space,there are still a large number of invalid actions.It often leads to the interruption of RL agents’wandering due to the selection of invalid actions,resulting in a significant decrease in the success rate of path mining.In order to improve the success rate of RL agents in the early stages of path search,this article proposes a knowledge reasoning method based on Deep Transfer Reinforcement Learning path(DTRLpath).Before supervised pre-training and retraining,a pre-task of searching for effective actions in a single step is added.The RL agent is first trained in the pre-task to improve its ability to search for effective actions.Then,the trained agent is transferred to the target reasoning task for path search training,which improves its success rate in searching for target task paths.Finally,based on the comparative experimental results on the FB15K-237 and NELL-995 datasets,it can be concluded that the proposed method significantly improves the success rate of path search and outperforms similar methods in most reasoning tasks.

A deep learning method based on prior knowledge with dual training for solving FPK equation

The evolution of the probability density function of a stochastic dynamical system over time can be described by a Fokker–Planck–Kolmogorov(FPK) equation, the solution of which determines the distribution of macroscopic variables in the stochastic dynamic system. Traditional methods for solving these equations often struggle with computational efficiency and scalability, particularly in high-dimensional contexts. To address these challenges, this paper proposes a novel deep learning method based on prior knowledge with dual training to solve the stationary FPK equations. Initially, the neural network is pre-trained through the prior knowledge obtained by Monte Carlo simulation(MCS). Subsequently, the second training phase incorporates the FPK differential operator into the loss function, while a supervisory term consisting of local maximum points is specifically included to mitigate the generation of zero solutions. This dual-training strategy not only expedites convergence but also enhances computational efficiency, making the method well-suited for high-dimensional systems. Numerical examples, including two different two-dimensional(2D), six-dimensional(6D), and eight-dimensional(8D) systems, are conducted to assess the efficacy of the proposed method. The results demonstrate robust performance in terms of both computational speed and accuracy for solving FPK equations in the first three systems. While the method is also applicable to high-dimensional systems, such as 8D, it should be noted that computational efficiency may be marginally compromised due to data volume constraints.

Strabismus Detection Based on Uncertainty Estimation and Knowledge Distillation

Strabismus significantly impacts human health as a prevalent ophthalmic condition.Early detection of strabismus is crucial for effective treatment and prognosis.Traditional deep learning models for strabismus detection often fail to estimate prediction certainty precisely.This paper employed a Bayesian deep learning algorithm with knowledge distillation,improving the model's performance and uncertainty estimation ability.Trained on 6807 images from two tertiary hospitals,the model showed significantly higher diagnostic accuracy than traditional deep-learning models.Experimental results revealed that knowledge distillation enhanced the Bayesian model’s performance and uncertainty estimation ability.These findings underscore the combined benefits of using Bayesian deep learning algorithms and knowledge distillation,which improve the reliability and accuracy of strabismus diagnostic predictions.

Embedding-based approximate query for knowledge graph

To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are classified according to the degrees of approximation required for different types of nodes.This classification transforms the query problem into three constraints,from which approximate information is extracted.Second,candidates are generated by calculating the similarity between embeddings.Finally,a deep neural network model is designed,incorporating a loss function based on the high-dimensional ellipsoidal diffusion distance.This model identifies the distance between nodes using their embeddings and constructs a score function.k nodes are returned as the query results.The results show that the proposed method can return both exact results and approximate matching results.On datasets DBLP(DataBase systems and Logic Programming)and FUA-S(Flight USA Airports-Sparse),this method exhibits superior performance in terms of precision and recall,returning results in 0.10 and 0.03 s,respectively.This indicates greater efficiency compared to PathSim and other comparative methods.

Effects of Health Education with Problem-Based Learning Approaches on the Knowledge, Attitude, Practice and Coping Skills of Women with High-Risk Pregnancies in Plateau Areas

Objective: Given the unique cultural background, way of life, and physical environment of the Tibetan Plateau, this study aims to investigate the effects of health education using problem-based learning (PBL) approaches on the knowledge, attitude, practice, and coping skills of women with high-risk pregnancies in this region. Methods: 76 high-risk pregnancy cases were enrolled at Tibet’s Linzhi People’s Hospital between September 2023 and April 2024. 30 patients admitted between September 2023 and December 2023 were selected as the control group and were performed with regular patient education. 46 patients admitted between January 2024 and April 2024 were selected as the observation group and were performed regular patient education with problem-based learning approaches. Two groups’ performance on their health knowledge, attitude, practice and coping skills before and after interventions were evaluated, and patient satisfaction were measured at the end of the study. Results: There was no statistical significance (P P P Conclusions: Health education with problem-based learning approaches is worth promoting as it can help high-risk pregnant women in plateau areas develop better health knowledge, attitude and practice and healthier coping skills. Also, it can improve patient sanctification.

Research Progress on Economic Forest Water Stress Based on Bibliometrics and Knowledge Graph

This study employed the bibliometric software CiteSpace 6.1.R6 to analyze the correlation between thermal infrared,spectral remote sensing technology,and the estimation of economic forest water stress.It aimed to review the development and current status of this field,as well as to identify future research trends.A search was conducted on the China National Knowledge Infrastructure(CNKI)database using the keyword“water stress”for relevant studies from 2003 to 2023.The visual analysis function of CNKI was used to generate the distribution of annual publication volume,and CiteSpace 6.1.R6 was utilized to create network maps illustrating collaboration among authors and institutions.The study also analyzed the hotspots and frontiers of economic forest water stress.As a result,a total of 6664 academic journal articles related to water stress were retrieved.Considerable collaboration networks were observed among scholars and institutions,with a focus on using crown temperature monitoring to diagnose crop water stress.Based on the research findings,it was evident that the primary research trend involved the use of thermal infrared and spectral remote sensing technology for estimating water stress,making it a future research hotspot.

Analysis on the Changes of Research Hotspots in the Prevention and Treatment of COVID-19 by Traditional Chinese Medicine Based on Knowledge Graph

Objective: To grasp the changing trend of research hotspots of traditional Chinese medicine in the prevention and treatment of COVID-19, and to better play the role of traditional Chinese medicine in the prevention and treatment of COVID-19 and other diseases. Methods: The research literature from 2020 to 2022 was searched in the CNKI database, and CiteSpace software was used for visual analysis. Results: The papers on the prevention and treatment of COVID-19 by traditional Chinese medicine changed from cases, overviews, reports, and efficacy studies to more in-depth mechanism research, theoretical exploration, and social impact analysis, and finally formed a theory-clinical-society Influence-institutional change and other multi-dimensional achievement systems. Conclusion: Analyzing the changing trends of TCM hotspots in the prevention and treatment of COVID-19 can fully understand the important value of TCM, take the coordination of TCM and Western medicine as an important means to deal with public health security incidents, and promote the exploration of the potential efficacy of TCM, so as to enhance the role of TCM in Applications in social stability, emergency security, clinical practice, etc.

Design of a sustainable development path in Chenzhou based on a knowledge graph

Sustainable urban development involves many fields with complex data types and rich semantic relationships,such as the economic,societal,and ecological fields.Knowledge graphs provide a new means for sustainable urban development research by leveraging their strengths in the construction of knowledge networks and display of knowledge associations.Focusing on Chenzhou,a resource-based city serving as a China’s Innovation Demonstration Zone for Sustainable Development Agenda,this study adopted a top-down approach,applying the“seven-step”and“skeleton”methods to construct an ontology for sustainable urban development through manual editing.A knowledge graph was constructed for Chenzhou’s sustainable development,comprising 515 nodes,3209 relations,and 28157 attributes.Sustainable measures and pathways were proposed based on this knowledge graph.The results showed that Chenzhou’s future sustainable development should focus on innovation,growth,emissions reduction,centering around high-quality and sustainable development.Promoting the transfer and transformation of scientific and technological achievements,accelerating the optimization and upgrading of industrial structures,and enhancing talent cultivation and recruitment will foster new quality productive forces,providing strong momentum and support for the high-quality and sustainable development of Chenzhou.To accelerate the green economy transition,Chenzhou should improve the market-oriented allocation system for resources and environmental factors,explore the“gross ecosystem product+eco environment-oriented development”project implementation model,encourage enterprises to adopt environmental,social,and governance principles,and foster synergies between supply and demand.Furthermore,coordinating Chenzhou’s low-carbon city pilot projects and constructing carbon sequestration pathways that leverage nature-based solutions will help implement the“dual carbon”actions and enhance the city’s ability to respond to climate change.

KGTLIR:An Air Target Intention Recognition Model Based on Knowledge Graph and Deep Learning

As a core part of battlefield situational awareness,air target intention recognition plays an important role in modern air operations.Aiming at the problems of insufficient feature extraction and misclassification in intention recognition,this paper designs an air target intention recognition method(KGTLIR)based on Knowledge Graph and Deep Learning.Firstly,the intention recognition model based on Deep Learning is constructed to mine the temporal relationship of intention features using dilated causal convolution and the spatial relationship of intention features using a graph attention mechanism.Meanwhile,the accuracy,recall,and F1-score after iteration are introduced to dynamically adjust the sample weights to reduce the probability of misclassification.After that,an intention recognition model based on Knowledge Graph is constructed to predict the probability of the occurrence of different intentions of the target.Finally,the results of the two models are fused by evidence theory to obtain the target’s operational intention.Experiments show that the intention recognition accuracy of the KGTLIRmodel can reach 98.48%,which is not only better than most of the air target intention recognition methods,but also demonstrates better interpretability and trustworthiness.

Knowledge Graph Based Method for Tracing Quality of Aerospace Products

Nowadays,the internal structure of spacecraft has been increasingly complex.As its“lifeline”,cables require extensive manpower and resources for manual testing,and it is challenging to quickly and accurately locate quality problems and find solutions.To address this problem,a knowledge graph based method is employed to extract multi-source heterogeneous cable knowledge entities.The method utilizes the bidirectional encoder representations from transformers(BERT)network to embed word vectors into the input text,then extracts the contextual features of the input sequence through the bidirectional long short-term memory(BiLSTM)network,and finally inputs them into the conditional random field(CRF)network to predict entity categories.Simultaneously,by using the entities extracted by this model as the data layer,a knowledge graph based method has been constructed.Compared to other traditional extraction methods,the entity extraction method used in this study demonstrates significant improvements in metrics such as precision,recall and an F1 score.Ultimately,employing cable test data from a particular aerospace precision machining company,the study has constructed the knowledge graph based method in the field to achieve visualized queries and the traceability and localization of quality problems.

Knowledge and practice skills on home-based urinary catheter care among parents of under-five children with urinary catheter

Objectives:The objectives of this study were to assess the knowledge and practice skills on home-based urinary catheter care among parents of under-five children with urinary catheter.Materials and Methods:This cross-sectional study was conducted from June 1,2021,to September 11,2021,in a tertiary hospital in north India.Purposive sampling was used to select 50 participants.Three instruments were employed for data collection after fulfilling sample criteria;for baseline information demographic tool,knowledge questionnaires,and a practice checklist.Data were analyzed using descriptive and inferential statistics.Results:On assessment of 50 participants,the majority of parents aged above 30 years(74%).Most of the participants were male(82%),graduated(38%),and working in the private sector(58%).Similarly,two-thirds of participants were residing in a nuclear family(64%)with a single child 32(64%)and family income<5000 rupees per month(60%).The mean score of knowledge was 1.94±0.81 and that of practice skills was 1.98±0.85 on home-based care.Regression analysis showed that knowledge of parents was significantly associated with qualification(β:1.821,P=0.002).Similarly,association of practice skills of parents with gender(β:1.235,P=0.050)and qualification(β:1.889,P=0.00)was significant.Conclusion:The general findings of our study showed that parents’education and occupation played a significant role in a child’s care.Parental education and catheter care skills positively affect the child and reduce readmission rates.

A Novel Tensor Decomposition-Based Efficient Detector for Low-Altitude Aerial Objects With Knowledge Distillation Scheme

Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.

Knowledge Graph Analysis of International Chinese Language Textbooks Based on CiteSpace

Drawing upon relevant papers from Chinese core journals and CSSCI source journals in the CNKI China Academic Journals Full-Text Database spanning from 1992 to 2023,this study utilizes CiteSpace as a research tool to visually analyze the knowledge graph structure of research on international Chinese language textbooks in China.The study maps out the publication timeline,authors,institutions,collaborative networks,and keywords pertaining to research on international Chinese language textbooks.The findings indicate that research on international Chinese language textbooks commenced early and continues to maintain a certain level of research interest,yet lacks sufficient research output.Research institutions predominantly reside in universities and publishing groups specializing in language or education,with collaboration between institutions being relatively scarce.High-frequency keywords in recent research on international Chinese language textbooks include“Chinese language textbooks for the Foreigners,”“Chinese language textbooks,”“Teaching Chinese Language for the Foreigners,”“Textbook compilation,”“International Chinese Language Education and Localization,”which reflect a diversified research perspective with interdisciplinary trends.Future research priorities encompass research on localization,customization of textbooks,and evaluation of textbooks which represent forefront directions of research.

Construction Method for Performance Management Curriculum Content System Based on Knowledge Graph

Performance Management is the core course of human resource management major,but its knowledge points lack multi-dimensional correlations.There are problems such as scattered content and unclear system,and it is urgent to reconstruct the content system of the course.Knowledge graph technology can integrate massive and scattered information into an organic structure through semantic correlation and reasoning.The application of knowledge graph to education and teaching can promote scientific and personalized teaching evaluation and better realize individualized teaching.This paper systematically combs the knowledge points of Performance Management course and forms a comprehensive knowledge graph.The knowledge point is associated with specific questions to form the problem map of the course,and then the knowledge point is further associated with the ability target to form the ability map of the course.Then,the knowledge point is associated with teaching materials,question bank and expansion resources to form a systematic teaching database,thereby giving the method of building the content system of Performance Management course based on the knowledge map.This research can be further extended to other core management courses to realize the deep integration of knowledge graph and teaching.

Research Progress and Suggestions on China’s Drug Registration Management Based on CiteSpace Knowledge Maps

Objective To analyze the research status and hot spots in the field of drug registration in China,and to provide some suggestions for the follow-up research.Methods CiteSpace was used to conduct literature quantitative analysis on 684 related articles from 2012 to 2022,and the knowledge map was drawn.Based on this,the main characteristics and development trends of the related studies were summarized.Results and Conclusion The number of articles published was closely related to the regulatory policy of drug registration reform.The authors of these articles did not have good continuity.Besides,research hot spots were closely related to the actual work,which was mainly around the improvement of the review and approval policy,encouraging innovative drug research and development,improving the level of new drug development and other directions.The follow-up studies should further strengthen the continuity of research and inter-agency collaboration.In addition,biomedical registration may become a new research focus in the future.

Capacity Set Optimization of Knowledge-Based Enterprise and Its Organizational IQ

In this paper, the knowledge based enterprise is considered as an organism, which possesses a set of capabilities. The organizational structure model of knowledge based enterprise organism is described in order to possess the essential capacity set. A dynamic capacity set is defined and analyzed based on the definition of the growth and development for knowledge based enterprise organism. The structure of the capacity base, a subset of the capacity set, is optimized for different periods of the organism ...