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.

Multi-Domain Malicious Behavior Knowledge Base Framework for Multi-Type DDoS Behavior Detection

Due to the many types of distributed denial-of-service attacks(DDoS)attacks and the large amount of data generated,it becomes a chal-lenge to manage and apply the malicious behavior knowledge generated by DDoS attacks.We propose a malicious behavior knowledge base framework for DDoS attacks,which completes the construction and application of a multi-domain malicious behavior knowledge base.First,we collected mali-cious behavior traffic generated by five mainstream DDoS attacks.At the same time,we completed the knowledge collection mechanism through data pre-processing and dataset design.Then,we designed a malicious behavior category graph and malicious behavior structure graph for the characteristic information and spatial structure of DDoS attacks and completed the knowl-edge learning mechanism using a graph neural network model.To protect the data privacy of multiple multi-domain malicious behavior knowledge bases,we implement the knowledge-sharing mechanism based on federated learning.Finally,we store the constructed knowledge graphs,graph neural network model,and Federated model into the malicious behavior knowledge base to complete the knowledge management mechanism.The experimental results show that our proposed system architecture can effectively construct and apply the malicious behavior knowledge base,and the detection capability of multiple DDoS attacks occurring in the network reaches above 0.95,while there exists a certain anti-interference capability for data poisoning cases.

Ontology modeling of semantics in social media:Public issue knowledge base (PIKB)of the Weibo

Purpose:This study aims to construct an ontology to model the semantics of social media streams,in particular,trending topics and public issues.Design/methodology/approach:Our knowledge base included 10 public events and topics from Weibo respectively,which were collected through keyword search and a crawler program.We used a semi-automatic approach to model and annotate the semantics in social media,and adapted the multi-layered ontology to refine the design based on previous researches,then we used named entity recognition(NER) to extract entities to instantiate the ontology.Relationships were extracted based on co-occurrence measures.Finally,we manually conducted post-filtering evaluation and edited the extracted entities and relationships.Findings:An initial assessment demonstrated that our multi-layered ontology supports various types of queries and analyses in the public issue knowledge base(PIKB),which can serve as an effective tool to query,understand and trace public issues.Research limitations:Manual involvement cannot meet the requirements for challenges of sustainable developments.Since the relationships extracted are fully based on the co-occurrence of entities,rich semantic relationships,such as how much the key players have been involved,could not be fully reflected.Besides,the user evaluation is necessary for further ontology assessment.Practical implications:The PIKB can be used by regular Web users and policy makers to query,understand,and make sense of public events and topics.The methodology and reusable ontology model are useful for institutions that are interested in making use of the social media data.Originality/value:In this study,a multi-layered ontology is applied to model the evolving semantics of public events and trending topics in social media,and the semi-automatic approach could make it possible to extract entities and relationships from large amount of unstructured short texts of user generated content(UGC) from social media.

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

Research and Design of Parallel Knowledge Base Machine- PKBM95

ＲｅｓｅａｒｃｈａｎｄＤｅｓｉｇｎｏｆＰａｒａｌｌｅｌＫｎｏｗｌｅｄｇｅＢａｓｅＭａｃｈｉｎｅ－ＰＫＢＭ９５￥ＧＵＯＦｕｓｈｕｎ；ＬＩＡＯＭｉｎｇｈｏｎｇ；ＳＯＮＧＺｈｅｎ；ＷＵＺｈｉｇａｎｇ（郭福顺，廖明宏，宋震，吴志刚）（Ｄｅｐｔ．ｏｆＣｏｍｐｕ...

A Health State Prediction Model Based on Belief Rule Base and LSTM for Complex Systems

In industrial production and engineering operations,the health state of complex systems is critical,and predicting it can ensure normal operation.Complex systems have many monitoring indicators,complex coupling structures,non-linear and time-varying characteristics,so it is a challenge to establish a reliable prediction model.The belief rule base(BRB)can fuse observed data and expert knowledge to establish a nonlinear relationship between input and output and has well modeling capabilities.Since each indicator of the complex system can reflect the health state to some extent,the BRB is built based on the causal relationship between system indicators and the health state to achieve the prediction.A health state prediction model based on BRB and long short term memory for complex systems is proposed in this paper.Firstly,the LSTMis introduced to predict the trend of the indicators in the system.Secondly,the Density Peak Clustering(DPC)algorithmis used todetermine referential values of indicators for BRB,which effectively offset the lack of expert knowledge.Then,the predicted values and expert knowledge are fused to construct BRB to predict the health state of the systems by inference.Finally,the effectiveness of the model is verified by a case study of a certain vehicle hydraulic pump.

Neuro-Knowledge-Based Expert System (NKBES) for Optimal Scheming of Die Casting Process

We develop a neuro-knowledge-based expert system (NKBES) frame in this work. The system mainly concerns with decision of gating system and die casting machine based on a neuro-inference engine launched under the MATLAB software environment. For enhancement of reasoning agility, an error back-propagation neural network was applied. A rapidly convergent adaptive learning rate (ALR) and a momentum-based error back-propagation algorithm was used to conduct neuro-reasoning. The working effect of the system was compared to a conventional expert system that is based on a two-way (forward and backward) chaining inference mechanism. As the reference, the present paper provided the neural networks sum-squared error (S5E) and ALR vs iterative epoch curves of process planning case mentioned above. The study suggests that the neuro-modeling optimization application to die casting process design has good feasibility, and based on that a novel and effective intelligent expert system can be launched at low cost.

合作数字参考服务中的知识库建设——DREW与DCVRS的Knowledge Base

知识库建设是合作数字参考服务中非常重要的部分,国外雪城信息研究所的数字参考电子仓库项目和我国CALIS分布式联合虚拟参考系统的知识库建设是非常典型的代表,深入分析其发展趋势,对以后数字参考服务的知识库建设具有指导意义。

知识库系统KBASE＋的数据模型,语言及实现

本文讨论具有面向对象特征的知识库系统ＫＢＡＳＥ＋的数据模型、语言及实现．ＫＢＡＳＥ＋的数据模型可以方便地支持对象标识、类层次、多继承等面向对象概念．描述性查询语言ＫＢＬ是扩充的ＰＡＴＡＬＯＧ．本文重构了ＫＢＬ语义理论框架，提出了解决属性继承和实例继承的方案，说明了ＫＢＬ程序可以转换成语义等价的ＤＡＴＡＬＯＧ程序．

KBASE－P知识库系统的设计与实现

当前的实用知识库系统研究是将知识库查询语言嵌入到一个过程语言中．ＫＢＡＳＥ－Ｐ是一个通用的知识库程序设计语言．ＫＢＡＳＥ－Ｐ以ＫＢＡＳＥ作为查询语言，以ＦＤ－ＰＲＯＬＯＧ（我们开发的一个ＰＲＯＬＯＧ扩充）为过程性的宿主语言执行１／Ｏ和ＤＢ更新操作（用扩充的内部谓词）．由于良好的设计和实现，查询语言与宿主语言之间的阻抗不匹配问题相对较小．因而，ＫＢＡＳＥ－Ｐ是一个比较实用的知识库程序设计语言．ＫＢＡＳＥ－Ｐ系统支持逻辑程序设计语言（ＫＢＡＳＥ－Ｐ语言）的程序开发，提供了文本编辑、文件管理、谓词管理、事实操作、Ｄａｔａｌｏｇ查询、ＳＱＬ查询等功能．本文详细介绍了ＫＢＡＳＥ－Ｐ系统的设计和实现．

Construction of carbonate reservoir knowledge base and its application in fracture-cavity reservoir geological modeling

To improve the efficiency and accuracy of carbonate reservoir research,a unified reservoir knowledge base linking geological knowledge management with reservoir research is proposed.The reservoir knowledge base serves high-quality analysis,evaluation,description and geological modeling of reservoirs.The knowledge framework is divided into three categories:technical service standard,technical research method and professional knowledge and cases related to geological objects.In order to build a knowledge base,first of all,it is necessary to form a knowledge classification system and knowledge description standards;secondly,to sort out theoretical understandings and various technical methods for different geologic objects and work out a technical service standard package according to the technical standard;thirdly,to collect typical outcrop and reservoir cases,constantly expand the content of the knowledge base through systematic extraction,sorting and saving,and construct professional knowledge about geological objects.Through the use of encyclopedia based collaborative editing architecture,knowledge construction and sharing can be realized.Geological objects and related attribute parameters can be automatically extracted by using natural language processing(NLP)technology,and outcrop data can be collected by using modern fine measurement technology,to enhance the efficiency of knowledge acquisition,extraction and sorting.In this paper,the geological modeling of fracture-cavity reservoir in the Tarim Basin is taken as an example to illustrate the construction of knowledge base of carbonate reservoir and its application in geological modeling of fracture-cavity carbonate reservoir.

KNOWLEDGE AND XML BASED CAPP SYSTEM

In order to enhance the intelligent level of system and improve the interaetivity with other systems, a knowledge and XML based computer aided process planning （CAPP） system is implemented. It includes user management, bill of materials（BOM） management, knowledge based process planning, knowledge management and database maintaining sub-systems. This kind of nesting knowledge representation method the system provided can represent complicated arithmetic and logical relationship to deal with process planning tasks. With the representation and manipulation of XML based technological file, the system solves some important problems in web environment such as information interactive efficiency and refreshing of web page. The CAPP system is written in ASP VBScript, JavaScript, Visual C＋＋ languages and Oracle database. At present, the CAPP system is running in Shenyang Machine Tools. The functions of it meet the requirements of enterprise production.

A Knowledge Base System for Operation Optimization: Design and Implementation Practice for the Polyethylene Process

Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the ef ciency of reuse of information and knowledge two critical ele- ments in polyethylene smart manufacturing. In this paper, we propose an overall structure for a knowl- edge base based on practical customer demand and the mechanism of the polyethylene process. First, an ontology of the polyethylene process constructed using the seven-step method is introduced as a carrier for knowledge representation and sharing. Next, a prediction method is presented for the molecular weight distribution (MWD) based on a back propagation (BP) neural network model, by analyzing the relationships between the operating conditions and the parameters of the MWD. Based on this network, a differential evolution algorithm is introduced to optimize the operating conditions by tuning the MWD. Finally, utilizing a MySQL database and the Java programming language, a knowledge base system for the operation optimization of the polyethylene process based on a browser/server framework is realized.

Development of an Ontology-Based Knowledge Network by Interconnecting Soil/Water Concepts/Properties, Derived from Standards Methods and Published Scientific References Outlining Infiltration/Percolation Process of Contaminated Water

The present work deals with the development of an Ontology-Based Knowledge Network of soil/water physicochemical & biological properties (soil/water concepts), derived from ASTM Standard Methods (ASTMi,n) and relevant scientific/applicable references (published papers—PPi,n) to fill up/bridge the gap of the information science between cited Standards and infiltration discipline conceptual vocabulary providing accordingly a dedicated/internal Knowledge Base (KB). This attempt constitutes an innovative approach, since it is based on externalizing domain knowledge in the form of Ontology-Based Knowledge Networks, incorporating standardized methodology in soil engineering. The ontology soil/water concepts (semantics) of the developed network correspond to soil/water physicochemical & biological properties, classified in seven different generations that are distinguished/located in infiltration/percolation process of contaminated water through soil porous media. The interconnections with arcs between corresponding concepts/properties among the consecutive generations are defined by the relationship of dependent and independent variables. All these interconnections are documented according to the below three ways: 1) dependent and independent variables interconnected by using the logical operator “depends on” quoting existent explicit functions and equations;2) dependent and independent variables interconnected by using the logical operator “depends on” quoting produced implicit functions, according to Rayleigh’s method of indices;3) dependent and independent variables interconnected by using the logical operator “related to” based on a logical dependence among the examined nodes-concepts-variables. The aforementioned approach provides significant advantages to semantic web developers and web users by means of prompt knowledge navigation, tracking, retrieval and usage.

A Knowledge-based System for the Analysis of the Ability of Paying back Loans

This paper describes the development of a knowledgebased system (KBS) for determining whether or not, and under what conditions, a bank Ioan officer should grant a business loan to a company. The prototype system developed focuses on what is bank loans risks management, how to prevent risk by the analysis of the ability of paying back loans. The paper makes the structural analysis involved in the system's decision situation, the structured situation diagram or model, dependency diagram and the document needed by the KBS prototype system thus are developed. Through testing the samples from loan business, the quality for the analysis of the ability of paying back loans can be effectively evaluated by the KBS prototype system.

Knowledge-Based Classification in Automated Soil Mapping

A machine-learning approach was developed for automated building of knowledge bases for soil resourcesmapping by using a classification tree to generate knowledge from training data. With this method, buildinga knowledge base for automated soil mapping was easier than using the conventional knowledge acquisitionapproach. The knowledge base built by classification tree was used by the knowledge classifier to perform thesoil type classification of Longyou County, Zhejiang Province, China using Landsat TM bi-temporal imagesand GIS data. To evaluate the performance of the resultant knowledge bases, the classification results werecompared to existing soil map based on a field survey. The accuracy assessment and analysis of the resultantsoil maps suggested that the knowledge bases built by the machine-learning method was of good quality formapping distribution model of soil classes over the study area.

Experts' Knowledge Fusion in Model-Based Diagnosis Based on Bayes Networks

In previous researches on a model-based diagnostic system, the components are assumed mutually independent. Howerver , the assumption is not always the case because the information about whether a component is faulty or not usually influences our knowledge about other components. Some experts may draw such a conclusion that 'if component m 1 is faulty, then component m 2 may be faulty too'. How can we use this experts' knowledge to aid the diagnosis? Based on Kohlas's probabilistic assumption-based reasoning method, we use Bayes networks to solve this problem. We calculate the posterior fault probability of the components in the observation state. The result is reasonable and reflects the effectiveness of the experts' knowledge.

New Knowledge-based Genetic Algorithm for Excavator Boom Structural Optimization

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Knowledge-based bridge detection from SAR images

Automatic bridge detection is an important application of SAR images. Differed from the classical CFAR method, a new knowledge-based bridge detection approach is proposed. The method not only uses the backscattering intensity difference between targets and background but also applies the contextual information and spatial relationship between objects. According to bridges＇ special characteristics and scattering properties in SAR images, the new knowledge-based method includes three processes： river segmentation, potential bridge areas detection and bridge discrimination. The application to AIRSAR data shows that the new method is not sensitive to rivers＇ shape. Moreover, this method can detect bridges successfully when river segmentation is not very exact and is more robust than the radius projection method.

Equipment selection knowledge base system for industrial styrene process

Equipment selection for industrial process usually requires the extensive participation of industrial experts and technologists, which causes a serious waste of resources. This work presents an equipment selection knowledge base system for industrial styrene process(S-ESKBS) based on the ontology technology. This structure includes a low-level knowledge base and a top-level interactive application. As the core part of the S-ESKBS, the low-level knowledge base consists of the equipment selection ontology library, equipment selection rule set and Pellet inference engine. The top-level interactive application is implemented using S-ESKBS, including the parsing storage layer, inference query layer and client application layer. Case studies for the industrial styrene process equipment selection of an analytical column and an alkylation reactor are demonstrated to show the characteristics and implementability of the S-ESKBS.