Assessing healthcare workers’knowledge and confidence in the diagnosis,management and prevention of Monkeypox

BACKGROUND Monkeypox(Mpox),is a disease of global public health concern,as it does not affect only countries in western and central Africa.AIM To assess Burundi healthcare workers(HCWs)s’level of knowledge and confidence in the diagnosis and management of Mpox.METHODS We conducted a cross-sectional study via an online survey designed mainly from the World Health Organization course distributed among Burundi HCWs from June-July 2023.The questionnaire comprises 8 socioprofessional-related questions,22 questions about Mpox disease knowledge,and 3 questions to assess confidence in Mpox diagnosis and management.The data were analyzed via SPSS software version 25.0.A P value<0.05 was considered to indicate statistical significance.RESULTS The study sample comprised 471 HCWs who were mainly medical doctors(63.9%)and nurses(30.1%).None of the 22 questions concerning Mpox knowledge had at least 50%correct responses.A very low number of HCWs(17.4%)knew that Mpox has a vaccine.The confidence level to diagnose(21.20%),treat(18.00%)or prevent(23.30%)Mpox was low among HCWs.The confidence level in the diagnosis of Mpox was associated with the HCWs’age(P value=0.009),sex(P value<0.001),work experience(P value=0.002),and residence(P value<0.001).The confidence level to treat Mpox was significantly associated with the HCWs’age(P value=0.050),sex(P value<0.001),education(P value=0.033)and occupation(P value=0.005).The confidence level to prevent Mpox was associated with the HCWs’education(P value<0.001),work experience(P value=0.002),residence(P value<0.001)and type of work institution(P value=0.003).CONCLUSION This study revealed that HCWs have the lowest level of knowledge regarding Mpox and a lack of confidence in the ability to diagnose,treat or prevent it.There is an urgent need to organize continuing medical education programs on Mpox epidemiology and preparedness for Burundi HCWs.We encourage future researchers to assess potential hesitancy toward Mpox vaccination and its associated factors.

Global systematic review and meta-analysis of knowledge, attitudes, and practices towards dengue fever among the general population

Objective:To determine the global level of knowledge,attitudes,and practices towards dengue fever among the general population.Methods:To complete this systematic review and meta-analysis,a thorough search for pertinent English-language literature was undertaken during the study's extension until October 2023.The search used Google Scholar,Scopus,PubMed/MEDLINE,Science Direct,Web of Science,EMBASE,Springer,and ProQuest.A quality assessment checklist developed using a modified Newcastle-Ottawa Scale for the cross-sectional study was used to evaluate the risk of bias in the included papers.Inverse variance and Cochran Q statistics were employed in the STATA software version 14 to assess study heterogeneity.When there was heterogeneity,the Dersimonian and Liard random-effects models were used.Results:59 Studies totaling 87353 participants were included in this meta-analysis.These investigations included 86278 participants in 55 studies on knowledge,20196 in 33 studies on attitudes,and 74881 in 29 studies on practices.The pooled estimates for sufficient knowledge,positive attitudes,and dengue fever preventive behaviors among the general population were determined as 40.1%(95%CI 33.8%-46.5%),46.8%(95%CI 35.8%-58.9%),and 38.3%(95%CI 28.4%-48.2%),respectively.Europe exhibits the highest knowledge level at 63.5%,and Africa shows the lowest at 20.3%.Positive attitudes are most prevalent in the Eastern Mediterranean(54.1%)and Southeast Asia(53.6%),contrasting sharply with the Americas,where attitudes are notably lower at 9.05%.Regarding preventive behaviors,the Americas demonstrate a prevalence of 12.1%,Southeast Asia at 28.1%,Western Pacific at 49.6%,Eastern Mediterranean at 44.8%,and Africa at 47.4%.Conclusions:Regional disparities about the knowledge,attitude and preventive bahaviors are evident with Europe exhibiting the highest knowledge level while Africa has the lowest.These findings emphasize the importance of targeted public health interventions tailored to regional contexts,highlighting the need for region-specific strategies to enhance dengue-related knowledge and encourage positive attitudes and preventive behaviors.

The DAO to MetaControl for MetaSystems in Metaverses:The System of Parallel Control Systems for Knowledge Automation and Control Intelligence in CPSS

An investigation and outline of MetaControl and DeControl in Metaverses for control intelligence and knowledge automation are presented.Prescriptive control with prescriptive knowledge and parallel philosophy is proposed as the starting point for the new control philosophy and technology,especially for computational control of metasystems in cyberphysical-social systems.We argue that circular causality,the generalized feedback mechanism for complex and purposive systems,should be adapted as the fundamental principle for control and management of metasystems with metacomplexity in metaverses.Particularly,an interdisciplinary approach is suggested for MetaControl and DeControl as a new form of intelligent control based on five control metaverses:MetaVerses,MultiVerses,InterVerses,TransVerse,and DeepVerses.

A novel fracture-cavity reservoir outcrop geological knowledge base construction method considering parameter collection and processing,mutual transformation of data-knowledge,application and update

This study endeavors to formulate a comprehensive methodology for establishing a Geological Knowledge Base(GKB)tailored to fracture-cavity reservoir outcrops within the North Tarim Basin.The acquisition of quantitative geological parameters was accomplished through diverse means such as outcrop observations,thin section studies,unmanned aerial vehicle scanning,and high-resolution cameras.Subsequently,a three-dimensional digital outcrop model was generated,and the parameters were standardized.An assessment of traditional geological knowledge was conducted to delineate the knowledge framework,content,and system of the GKB.The basic parameter knowledge was extracted using multiscale fine characterization techniques,including core statistics,field observations,and microscopic thin section analysis.Key mechanism knowledge was identified by integrating trace elements from filling,isotope geochemical tests,and water-rock simulation experiments.Significant representational knowledge was then extracted by employing various methods such as multiple linear regression,neural network technology,and discriminant classification.Subsequently,an analogy study was performed on the karst fracture-cavity system(KFCS)in both outcrop and underground reservoir settings.The results underscored several key findings:(1)Utilization of a diverse range of techniques,including outcrop observations,core statistics,unmanned aerial vehicle scanning,high-resolution cameras,thin section analysis,and electron scanning imaging,enabled the acquisition and standardization of data.This facilitated effective management and integration of geological parameter data from multiple sources and scales.(2)The GKB for fracture-cavity reservoir outcrops,encompassing basic parameter knowledge,key mechanism knowledge,and significant representational knowledge,provides robust data support and systematic geological insights for the intricate and in-depth examination of the genetic mechanisms of fracture-cavity reservoirs.(3)The developmental characteristics of fracturecavities in karst outcrops offer effective,efficient,and accurate guidance for fracture-cavity research in underground karst reservoirs.The outlined construction method of the outcrop geological knowledge base is applicable to various fracture-cavity reservoirs in different layers and regions worldwide.

Prioritizing sustainable development goals and linking them to ecosystem services:A global expert’s knowledge evaluation

The implementation of strategies to achieve the Sustainable Development Goals(SDGs)is frequently hindered by potential trade-offs between priorities for either environmental protection or human well-being.However,ecosystem services(ES)-based solutions can offer possible co-benefits for SDGs implementation that are often overlooked or underexploited.In this study,we cover this gap and investigate how experts from different countries value the SDGs and relate them with ES.A total of 66 countries participated to the survey,and answers were grouped into three macro-regions:Asia;Europe,North America,and Oceania(ENO);Latin America,Caribbean and Africa(LA).Results show that the most prioritized SDGs in the three macro-regions are usually those related to essential material needs and environmental conditions,such as SDG2(Zero Hunger),SDG1(No Poverty),and SDG6(Clean Water).At a global scale,the number of prioritized synergies between SDGs and ES largely exceeded trade-offs.The highest amount of synergies was observed for SDG1(No Poverty),mainly with SDG2,SDG3(Good Health),SDG5(Gender Equality),and SDG8(Economic Growth).Other major synergies among SDGs include SDG14-15(Life below water-Life on land),SDG5-10(Gender Equity-Reduced Inequality),and SDG1-2(No poverty-Zero Hunger).At a global scale,SDG15,SDG13,SDG14,and SDG6 were closely related to ES like climate regulation,freshwater,food,water purification,biodiversity,and education.SDG11(Sustainable Cities)and SDG3 were also relevant in Asia and in LA,respectively.Overall,this study shows the potential to couple future policies that can implement SDGs’strategies while adopting ES-based solutions in different regions of the world.

Construction of fault diagnosis system for control rod drive mechanism based on knowledge graph and Bayesian inference

Knowledge graph technology has distinct advantages in terms of fault diagnosis.In this study,the control rod drive mechanism(CRDM)of the liquid fuel thorium molten salt reactor(TMSR-LF1)was taken as the research object,and a fault diagnosis system was proposed based on knowledge graph.The subject–relation–object triples are defined based on CRDM unstructured data,including design specification,operation and maintenance manual,alarm list,and other forms of expert experience.In this study,we constructed a fault event ontology model to label the entity and relationship involved in the corpus of CRDM fault events.A three-layer robustly optimized bidirectional encoder representation from transformers(RBT3)pre-training approach combined with a text convolutional neural network(TextCNN)was introduced to facilitate the application of the constructed CRDM fault diagnosis graph database for fault query.The RBT3-TextCNN model along with the Jieba tool is proposed for extracting entities and recognizing the fault query intent simultaneously.Experiments on the dataset collected from TMSR-LF1 CRDM fault diagnosis unstructured data demonstrate that this model has the potential to improve the effect of intent recognition and entity extraction.Additionally,a fault alarm monitoring module was developed based on WebSocket protocol to deliver detailed information about the appeared fault to the operator automatically.Furthermore,the Bayesian inference method combined with the variable elimination algorithm was proposed to enable the development of a relatively intelligent and reliable fault diagnosis system.Finally,a CRDM fault diagnosis Web interface integrated with graph data visualization was constructed,making the CRDM fault diagnosis process intuitive and effective.

Survey and Prospect for Applying Knowledge Graph in Enterprise Risk Management

Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order.Amidst the challenges posed by intricate and unpredictable risk factors,knowledge graph technology is effectively driving risk management,leveraging its ability to associate and infer knowledge from diverse sources.This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios.Firstly,employing bibliometric methods,the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge graphs.In the succeeding section,systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge graphs.Objectively comparing and summarizing the strengths and weaknesses of each method,we provide recommendations for addressing the existing challenges in the construction process.Subsequently,categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards,and furnishing a detailed exposition on the applicability of technical routes and methods.Finally,the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed,and relevant improvement suggestions were proposed.Practitioners and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.

Knowledge, attitudes, and practices regarding Covid-19 and their relationship with Covid-19 booster vaccination status among women with infertility

Objective:To elucidate the relationship among knowledge,attitudes,and practices regarding Covid-19 and their relationship with booster vaccination status among women with infertility.Methods:This questionnaire-based cross-sectional study was performed online and offline among women with infertility who visited an infertility clinic in Jakarta,Indonesia.We assessed the patient’s knowledge,attitudes,and practices regarding Covid-19 and their relationship with booster vaccination status and sociodemographic profile.Results:A total of 178 subjects participated in this study,and most participants(92.6%)had received booster Covid-19 vaccines.From the questionnaire,74.2%had good knowledge,and 99.4%had good attitudes regarding Covid-19;however,only 57.9%of patients had good practices.A weak positive correlation existed between knowledge and attitudes(r=0.11,P=0.13)and a moderate negative correlation between attitudes and practices(r=-0.44,P=0.56).Participants’knowledge about vaccines and infertility was correlated with booster vaccination status(P=0.04).Academic background(P=0.01)and attitudes(P=0.01)were also correlated with booster vaccination status.The significant determinants of hesitance of receiving Covid-19 booster vaccines were high school education or below(OR=0.08,95%CI 0.02-0.36)and poor practices(OR=0.21,95%CI 0.05-0.95).Conclusions:The majority of the participants had received the Covid-19 booster vaccine and had good knowledge and attitudes but poor practices regarding Covid-19.Most participants had poor knowledge about the relationship between infertility and the Covid-19 vaccine.The general population should be more informed and reminded about practices to prevent Covid-19 and the relationship between vaccination and fertility to increase the number of people who receive Covid-19 booster vaccines.

A cross-sectional study to assess medication safety,knowledge,attitude,and practices regarding nutrition and medication among pregnant women

Objective:To assess pregnant women's knowledge,attitude,and practice regarding nutrition and medication usage,analyse the prescribing pattern,and categorize them based on the Food and Drug Administration(FDA)guidelines.Methods:A cross-sectional study was conducted with 264 pregnant women in the obstetrics and gynaecology department of a tertiary care hospital from October 2022 to August 2023.A knowledge,attitude,and practice(KAP)questionnaire was prepared in English language by the researchers and validated by an expert panel consisting of 12 members.The validated questionnaire was then translated into regional languages,Kannada and Malayalam.The reliability of the questionnaire was assessed with test-retest method with a representative sample population of 30 subjects(10 subjects for each language).The subjects'knowledge,attitude,and practice were evaluated using the validated KAP questionnaire.The safety of the medication was assessed using the FDA drug safety classification for pregnancy.Results:The mean scores for nutritional and medication usage knowledge,attitude,and practice were 4.14±1.15,4.50±1.09,and 3.00±1.47,respectively.Among 30 prescribed medications,3 belong to category A(no risk in human studies),8 belong to category B(no risk in animal studies),18 belong to category C(risk cannot be ruled out)and 1 drug is not classified.A significant association was observed between medication knowledge and practice(r=0.159,P=0.010).Conclusions:Most of the study population knows the need to maintain good dietary and medication practices during pregnancy.Counselling pregnant women regarding diet and medication usage is crucial in maternal care.

A Knowledge Model-and Growth Model-Based Decision Support System for Wheat Management

By applying the system analysis principle and mathematical modeling technique to knowledge expression system for crop cultural management, the fundamental relationships and quantitative algorithms of wheat growth and management indices to variety types, ecological environments and production levels were analysed and extracted, and a dynamic knowledge model with temporal and spatial characters for wheat management(WheatKnow)was developed. By adopting the soft component characteristics as non language relevance , re-utilization and portable system maintenance. and by further integrating the wheat growth simulation model(WheatGrow)and intelligent system for wheat management, a comprehensive and digital knowledge model, growth model and component-based decision support system for wheat management(MBDSSWM)was established on the platforms of Visual C++ and Visual Basic. The MBDSSWM realized the effective integration and coupling of the prediction and decision-making functions for digital crop management.

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.

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.

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.

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks

Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.

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.

Knowledge mapping of research on sustainable utilization of landscape resources:a case study of Guilin National Innovation Demonstration Zone for sustainable development in China

Worldwide interest has increasingly focused on the sustainable utilization of landscape as a resource in urban areas,emphasizing its ecological,cultural and social significance.This study examines Guilin City,China,as a representative case study due to its rich landscape resources and status as a national innovation demonstration zone for implementing the 2030 Agenda for Sustainable Development.This study uses bibliometric visualization tools like CiteSpace and VOSviewer to analyze research trends from 1980 to 2021 in the Chinese Academic Journal Network Publishing Database(CNKI).The results show increasing academic interest over three stages:initiation(1982-1997),exploration(1998-2004),and diversified development(2005-2021).Contributions are predominantly from local academic and tourism sectors,indicating a strong regional influence;however,relatively weak interinstitutional collaboration occurs,suggesting potential for more integrated research efforts.Primary research is also concentrated within economic disciplines,particularly tourism-related ones.The evolution of research frontiers reveals three main paths:urban development strategies,industrial economic theories and empirical validation,and ecosystem analysis and evaluation.A multidisciplinary approach and stronger collaborative efforts are crucial to enhance research on ecological values and empirical models while supporting evidence-based urban development strategies in Guilin City and comparable cities globally.

LKPNR: Large Language Models and Knowledge Graph for Personalized News Recommendation Framework

Accurately recommending candidate news to users is a basic challenge of personalized news recommendation systems.Traditional methods are usually difficult to learn and acquire complex semantic information in news texts,resulting in unsatisfactory recommendation results.Besides,these traditional methods are more friendly to active users with rich historical behaviors.However,they can not effectively solve the long tail problem of inactive users.To address these issues,this research presents a novel general framework that combines Large Language Models(LLM)and Knowledge Graphs(KG)into traditional methods.To learn the contextual information of news text,we use LLMs’powerful text understanding ability to generate news representations with rich semantic information,and then,the generated news representations are used to enhance the news encoding in traditional methods.In addition,multi-hops relationship of news entities is mined and the structural information of news is encoded using KG,thus alleviating the challenge of long-tail distribution.Experimental results demonstrate that compared with various traditional models,on evaluation indicators such as AUC,MRR,nDCG@5 and nDCG@10,the framework significantly improves the recommendation performance.The successful integration of LLM and KG in our framework has established a feasible way for achieving more accurate personalized news recommendation.Our code is available at https://github.com/Xuan-ZW/LKPNR.

Knowledge and discriminatory attitudes towards HIV/AIDS among the women of reproductive age group of Pakistan using the Multiple Indicator Cluster Survey(MICS)

Background Human immunodeficiency virus/acquired immunodeficiency syndrome(HIV/AIDS)has become a major worldwide public health issue,with a focus on developing nations.Despite having a very low HIV prevalence,South Asia faces serious issues with stigma and false information because of a lack of awareness.This stigma highlights significant gaps in popular awareness while also sustaining unfavorable attitudes towards those living with HIV/AIDS.Pakistan is ranked second in South Asia for the rapidly increasing AIDS epidemic.Thorough information and optimistic outlooks are essential for successful HIV/AIDS prevention,control,and treatment.But false beliefs about how HIV/AIDS spreads lead to negative perceptions,which highlights the need to look into how women’s knowledge and attitudes about HIV/AIDS in Pakistan are influenced by sociodemographic traits and autonomy.Methods The purpose of this study is to evaluate Pakistani women’s discriminatory attitudes and level of awareness on HIV/AIDS.This study used data(the women in reproductive age 15-49 years’dataset)from the Pakistan Multiple Indicator Cluster Survey to conduct an analytical cross-sectional analysis.To represent the respondents’attitudes and knowledge towards people living with HIV(PLHIV),two composite variables were developed and composite scored.Binary logistics regression was used to identify predictor variables and chi-square was used for bivariate analysis.Results The findings reveal that almost 90%of Pakistani women have poor knowledge and attitude with HIV/AIDS.In Punjab,72.8%of rural residents have low knowledge,whereas only 20.6%of young individuals(15-<25 years old)show the least amount of ignorance.Education is shown to be crucial,and“Higher”education is associated with superior knowledge.Urban dwellers in Khyber Pakhtunkhwa typically have more expertise.Knowledge of HIV is positively correlated with education;those with higher education levels know a lot more(odds ratio[OR]=5.419).Similarly,quintiles with greater incomes show a higher likelihood of knowing about HIV(OR=6.745).The study identifies age,wealth index,place of residence,educational attainment,and exposure to contemporary media as significant predictors influencing HIV knowledge and attitudes among women in these provinces.Conclusion The majority of respondents had negative opinions regarding the virus,and the majority of women in the study knew very little about HIV.Individuals who live in metropolitan areas,have higher incomes,are better educated,are exposed to contemporary media,and are generally more aware of HIV and have more positive attitudes towards HIV/AIDS,or PLHIV.The study found that,in comparison to those living in urban environments,those from rural areas with low socioeconomic level have a negative attitude and inadequate understanding.

Knowledge expression and reasoning process in an expert system for welding procedure qualification

After analyzing the welding procedure knowledge in Chinese national standards for welding procedure qualification of steel pressure vessel from the point of establishing expert system, it can be divided into five types of knowledge, i. e. practice, definition, regularity, process and description knowledge. The knowledge expression methods are established according to the different type of welding procedure knowledge. The reasoning process based on rule is adopted. And the reasoning engine is embedded among objects integrated with the knowledge base.