Effects of the information–knowledge–attitude–practice nursing model combined with predictability intervention on patients with cerebrovascular disease

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.

Information Comprehension Based on the New Knowledge Acquired by Cognitive Subject as the Fourth Dimension Set

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.

DPAL-BERT:A Faster and Lighter Question Answering Model

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

Multi-action-based approach for constructing knowledge map

To alleviate the information overload in the product design process,this work proposes a multiaction-based method for constructing knowledge map. Since the relationships of knowledge are implicit in the collected user activities,the method calculates the similarity according to the collected user activities.Three concepts,including knowledge,action and user,are explained first. Based on this,the similarity calculation method is illustrated in detail. The dependencies of actions and relations of the user are considered in the calculation method. Further,the approach of applying the constructed knowledge map to alleviate information overload is proposed. At last,the proposed method is validated by a knowledge search and result comparison experiment.

Expert knowledge maps for knowledge management: a case study inTraditional Chinese Medicine research

OBJECTIVE:To design a model to capture information on the state and trends of knowledge creation,at both an individual and an organizational level,in order to enhance knowledge management.METHODS:We designed a graph-theoretic knowledge model,the expert knowledge map(EKM),based on literature-based annotation.A case study in the domain of Traditional Chinese Medicine research was used to illustrate the usefulness of the model.RESULTS:The EKM successfully captured various aspects of knowledge and enhanced knowledge management within the case-study organization through the provision of knowledge graphs,expert graphs,and expert-knowledge biography.CONCLUSION:Our model could help to reveal thehot topics,trends,and products of the research done by an organization.It can potentially be used to facilitate knowledge learning,sharing and decision-making among researchers,academicians,students,and administrators of organizations.

Semantic Rule Based Image Visual Feature Ontology Creation

Multimedia is one of the important communication channels for mankind. Due to the advancement in technology and enormous growth of mankind, a vast array of multimedia data is available today. This has resulted in the obvious need for some techniques for retrieving these data. This paper will give an overview of ontology-based image retrieval system for asteroideae flower family domain. In order to reduce the semantic gap between the low-level visual features of an image and the high-level domain knowledge, we have incorporated a concept of multi-modal image ontology. So, the created asteroideae flower domain specific ontology would have the knowledge about the domain and the visual features. The visual features used to define the ontology are prevalent color,basic intrinsic pattern and contour gradient. In prevalent color extraction, the most dominant color from the images was identified and indexed. In order to determine the texture pattern for a particular flower, basic intrinsic patterns were used. The contour gradients provide the information on the image edges with respect to the image base. These feature values are embedded in the ontology at appropriate slots with respect to the domain knowledge. This paper also defines some of the query axioms which are used to retrieve appropriate information from the created ontology. This ontology can be used for image retrieval system in semantic web.