Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurr...Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.展开更多
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 me...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.展开更多
In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shippi...In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.展开更多
BACKGROUND Breast cancer is one of the most prevalent causes of morbidity and mortality worldwide,presenting an increasing public health challenge,particularly in lowincome and middle-income countries.However,data on ...BACKGROUND Breast cancer is one of the most prevalent causes of morbidity and mortality worldwide,presenting an increasing public health challenge,particularly in lowincome and middle-income countries.However,data on the knowledge,attitudes,and preventive practices regarding breast cancer and the associated factors among females in Wollo,Ethiopia,remain limited.AIM To assess the impact of family history(FH)of breast disease on knowledge,attitudes,and breast cancer preventive practices among reproductive-age females.METHODS A community-based cross-sectional study was conducted in May and June 2022 in Northeast Ethiopia and involved 143 reproductive-age females with FH of breast diseases and 209 without such a history.We selected participants using the systematic random sampling technique.We analyzed the data using Statistical Package for Social Science version 25 software,and logistic regression analysis was employed to determine odds ratios for variable associations,with statistical significance set at P<0.05.RESULTS Among participants with FH of breast diseases,the levels of knowledge,attitudes,and preventive practices were found to be 83.9%[95%confidence interval(CI):77.9-89.9],49.0%(95%CI:40.8-57.1),and 74.1%(95%CI:66.9-81.3),respectively.In contrast,among those without FH of breast diseases,these levels were significantly decreased to 10.5%(95%CI:6.4-14.7),32.1%(95%CI:25.7-38.4),and 16.7%(95%CI:11.7-21.8),respectively.This study also indicated that knowledge,attitudes,and preventive practices related to breast cancer are significantly higher among participants with FH of breast diseases compared to those without HF breast diseases.CONCLUSION Educational status,monthly income,and community health insurance were identified as significant factors associated with the levels of knowledge,attitudes,and preventive practices regarding breast cancer among reproductive-age females.展开更多
Let F_(1)be the virtual field consisting of one element and(Q,I)a string pair.In this paper,we study the representations of string pairs over the virtual field F_(1).It is proved that an indecomposable F_(1)-represent...Let F_(1)be the virtual field consisting of one element and(Q,I)a string pair.In this paper,we study the representations of string pairs over the virtual field F_(1).It is proved that an indecomposable F_(1)-representation is either a string representation or a band representation by using the coefficient quivers.It is worth noting that for a given band and a positive integer,there exists a unique band representation up to isomorphism.展开更多
Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challeng...Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.展开更多
Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and di...Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.展开更多
In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by pred...In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional strategies.Traditional knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential data.However,these models may neglect crucial short-term dynamics and other relevant features.This paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)architecture.We proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature extractors.This architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key aspects.Empirical evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge states.These results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.展开更多
Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI pre...Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.展开更多
Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate...Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.展开更多
Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays ...Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable,thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making.This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations.The research methodology begins with the compilation of small molecule databases,followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms,capturing patterns and salient features across extensive chemical spaces.The study then examines various drug discovery downstream tasks,including drug-target interaction(DTI)prediction,drug-target affinity(DTA)prediction,drug property(DP)prediction,and drug generation,all based on learned representations.The analysis concludes by highlighting challenges and opportunities associated with machine learning(ML)methods for molecular representation and improving downstream task performance.Additionally,the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine(TCM)medicinal substances and facilitating TCM target discovery.展开更多
Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,stressed that we should adhere to the“two integrations”(namely,integrating the basic tenets of Marxism with China’s specific realit...Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,stressed that we should adhere to the“two integrations”(namely,integrating the basic tenets of Marxism with China’s specific realities and fine traditional culture),root ourselves in Chinese soil,carry forward the Chinese cultural heritage,and strengthen the academic foundation.We should accelerate the building of an independent knowledge system for Chinese philosophy and social sciences,and formulate original concepts and develop systems of academic discipline,research and discourse,drawing on China’s rich experience of advancing human rights.In the face of changes of a magnitude not seen in a century,in the historic process of advancing the great rejuvenation of the Chinese nation on all fronts through Chinese modernization,we should and must strengthen our theoretical self-consciousness and confidence in the path of Chinese modernization.We need to enhance human rights research,develop the human rights theoretical system and paradigm that are based on Chinese realities and express Chinese voice,and an independent Chinese knowledge system for human rights.展开更多
Background: Blood transfusion (BT) is crucial to the provision of modern health care. However, blood is scarce and costly, and its use is associated with risks. Therefore, the medical professionals who handle it shoul...Background: Blood transfusion (BT) is crucial to the provision of modern health care. However, blood is scarce and costly, and its use is associated with risks. Therefore, the medical professionals who handle it should have adequate knowledge to ensure rational and safe utilization. The objective of the study was to determine the level of BT knowledge among junior medical doctors in Kenya. Methodology: A cross-sectional study was conducted among junior medical doctors working in Western Kenya. Data was collected using questionnaires from August 2021 to March 2022, and analysis was done by way of descriptive and inferential statistics. A p Results: A total of 150 medical doctors participated in the study. Males comprised 60% (n = 90), and the mean age of the participants was 29.9 (SD 3.6) with a range of 25 - 45 years. The mean knowledge score was 54.1% ± 16.4% and was associated with orientation (AOR = 3.157, 95% CI = 1.194 - 8.337). Conclusion: Blood transfusion knowledge among the doctors was suboptimal and was associated with pre-internship induction. There is a need for additional education in BT during all phases of medical training and practice, including orientation for medical interns.展开更多
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 confid...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.展开更多
The integration of digital tools and effective knowledge management practices is critical for enhancing administrative efficiency and institutional continuity in higher education. This study investigates the relations...The integration of digital tools and effective knowledge management practices is critical for enhancing administrative efficiency and institutional continuity in higher education. This study investigates the relationships between knowledge modeling, institutional memory, leadership styles, technology, and administrative efficiency at the University of Cape Coast (UCC). The study sought to identify the challenges and opportunities in integrating digital tools into administrative processes and to provide actionable recommendations for improvement. A mixed-methods research design was employed, combining quantitative analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) with qualitative thematic analysis of interviews. The findings revealed key challenges, including resistance to change, fragmented knowledge repositories, and inadequate funding, alongside opportunities such as centralized knowledge systems, cost-effective open-source tools, and capacity-building initiatives. The study highlights the importance of strategic leadership, robust policies, and investments in digital infrastructure to enhance administrative practices. Policy implications include the need for clear digital transformation guidelines and leadership training to foster innovation and collaboration. Recommendations include investing in scalable digital tools, implementing comprehensive capacity-building programs, and promoting stakeholder engagement to drive successful digital integration. These insights provide a roadmap for UCC and similar institutions seeking to optimize administrative efficiency through digital transformation.展开更多
Introduction: Breastfeeding is the best way to provide ideal nutrition for optimal infant growth and development. Objectives: The aim of our work was to assess the knowledge, attitudes and practices of mothers of chil...Introduction: Breastfeeding is the best way to provide ideal nutrition for optimal infant growth and development. Objectives: The aim of our work was to assess the knowledge, attitudes and practices of mothers of children aged 0 - 24 months on exclusive breastfeeding in the Central African Republic. Methods: This was a cross-sectional, descriptive and analytical study conducted from September 15 to October 15, 2024 among mothers of infants aged 0 to 24 months. Sociodemographic, obstetric and breastfeeding-related data were collected through individual interviews conducted during sensitizations on good feeding practices organized by the Tina Touadera Foundation. The chi2 test was used to test for relationships between variables, and the p significance level was set at 0.05. Results: The average age of the mothers surveyed was 27.67 years. 65.69% (n = 247) of mothers lived in urban areas and 55.85% (n = 210) were Muslim. 56.38% (n = 212) were living common-law and 34.04% (n = 128) were poor. Secondary-school mothers (44.42%, n = 167) and housewives (53.72%, n = 202) were in the majority. Exclusive breastfeeding (EBF) was correctly defined by 79.26% (n = 298) of mothers and actually practised in 24.20% (n = 91) of cases. The main source of information was a health professional in 75.36% (n = 304) of cases. Among the 285 mothers who practised mixed breastfeeding, lack of time (33.33%) was the main reason. They acknowledged having given water (100%), corn porridge (75.09%) and/or artificial milk (24.91%) before the first 6 months of life. The average time for introducing water was 2.2 months, and for porridge/formula 2.79 months. More than half the mothers (55.05%) said they did not know their infants’ weaning age. Factors positively influencing the use of EBF were age under 29, residence in an urban area, primiparity, having been informed about AME by a health professional, and being a housewife or shopkeeper (p Conclusion: Mothers’ level of knowledge was heterogeneous but insufficient overall. An effective system of information and education from pregnancy to the first six months of life is needed to promote breastfeeding.展开更多
In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with l...In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.展开更多
Introduction: Uterine fibroids are benign tumors that develop from the connective and muscular tissues of the uterus. Common among African-American women, patients suffering from them often arrive late to the hospital...Introduction: Uterine fibroids are benign tumors that develop from the connective and muscular tissues of the uterus. Common among African-American women, patients suffering from them often arrive late to the hospital in our African regions. This study aimed to investigate the knowledge and perception of uterine fibroids among women who came to the gynecology-obstetrics department of the Regional Hospital Center (CHR) Tsévié. Methodology: It was a cross-sectional descriptive study, with data collection conducted from May 7th to 20th, 2024, using systematic sampling. The study included all women present in the Gynecology-Obstetrics Department of CHR Tsévié during the study period who willingly and informedly consented to participate in the survey. Results: 362 women participated in the study. Among them, 36.8% had a secondary level, and 72.9% were Christians. About 97.5% had heard of uterine fibroids. In 63.5% of cases, their entourage was the principal source of information. The diagnostic methods mentioned by the women were ultrasound in 94.6% of cases, while prayers and occultism were also cited in 28% and 33.3% of cases, respectively. While 91.9% of the women considered the hospital, the place for treatment, some indicated that treatment would require plant-based approaches (46.8%) and prayers (26%). The cost of treatment was an obstacle for 85.4% of women, and 61.3% expressed fear of dying during surgery. There was a statistically significant relationship between treatment choice and religion. Conclusion: The majority of women had heard of uterine fibroids but had incorrect information about the treatment.展开更多
Non-communicable diseases (NCDs) are on the rise worldwide and in developing countries like Botswana. Unhealthy eating habits and lack of proper nutrition knowledge cause non-communicable diseases and affect adolescen...Non-communicable diseases (NCDs) are on the rise worldwide and in developing countries like Botswana. Unhealthy eating habits and lack of proper nutrition knowledge cause non-communicable diseases and affect adolescents. It is in adolescence that eating habits are formed that persist till adulthood. Lifestyle interventions are needed to curb NCDs in adolescents. This paper reports the findings of a study that aimed to validate a lifestyle intervention program and its effect on food intake, physical activity, and nutrition knowledge. It was a clustered randomized control trial study conducted in four (4) junior secondary schools. There were 46 participants, 21 in the control and 25 in the intervention arm, who were blindly assigned to each arm by a statistician. Information and skills on nutrition were imparted using the Information, Motivation, and Behavioral Skills model. The program was implemented for eight (8) weeks hourly after school. A questionnaire was used to collect data pre- and post-intervention. Number, proportion, percentage, and independent t-test (mean and SD or median and IQR, p-value) were calculated using numerical and categorical data. The findings showed that the lifestyle intervention was valid, and there was a slight decrease in the intake of sweets among participants in both trial arms (p = 0.066). There was no significant difference in terms of food intake. Only a small number of participants still ate a few fruits, and there was no change in vegetable intake in both trial arms (p = 0.641). There was no change in the intake of fried foods in both trail arms (p = 0.402). Regarding nutrition knowledge, there was a slight significant difference of p = 0.079 between the trial arms. Though the effect of the lifestyle intervention program was not statistically significant, the results are promising, especially if the duration could be increased to a longer period and a larger sample size included.展开更多
Background: Hepatitis B virus (HBV) is a primary reason for liver cancer and continues to be a worldwide public health issue. The likelihood of contracting HBV is greater in healthcare workers (HCWs) compared to indiv...Background: Hepatitis B virus (HBV) is a primary reason for liver cancer and continues to be a worldwide public health issue. The likelihood of contracting HBV is greater in healthcare workers (HCWs) compared to individuals who are not in healthcare professions. Medical students are classified as a high-risk demographic since, like HCWs, they often come into contact with bodily fluids and blood during their clinical training. By 2030, a greater proportion of people will have received HBV vaccinations, thereby halting the spread of new infections—The Somali Ministry of Health with the help of various agencies announced to eradicate hepatitis from Somalia. The priority actions are national hepatitis strategy, hepatitis survey, public awareness, training, and capacity building. Objectives: This study aims to assess the knowledge, attitude, and vaccination status of Hepatitis B infection among medical university students in Mogadishu, Somalia, 2024. Methods: Cross-sectional study design was used in this study and the survey was carried out among medical students enrolled in Universities from April 1, 2023 to June 30, 2023. The data was analyzed using SPSS version 26.0 software, Chi-square analysis and Logistic regression analysis to identify associations between demographic factors and HBV knowledge, attitudes, and vaccination status, as well as perspectives and immunization status concerning viral hepatitis. Results: The study achieved a response rate of (96%), with 230 participants. Most students (76.5%) were aged 26 - 30 years, and (60.8%) were male. Nearly half (48.7%) were in their third year of study, and the majority (36.1%) were from the Medicine and Surgery department. While 92.2% had heard of HBV, gaps in understanding were evident. About 37.8% erroneously believed HBV could spread via handshakes, and only 33.9% were aware HBV is treatable. Awareness of HBV’s severe complications, such as liver cirrhosis and liver cancer, was reported by 61.3%, and 83% understood that vaccination could prevent infection. Positive attitudes towards HBV vaccination were prevalent. Most participants (81.3%) supported vaccination before sexual activity, and 78.3% endorsed mandatory HBV vaccination policies for healthcare workers. However, 87.4% expressed concerns about the vaccine promoting unsafe sexual behavior, and 96.1% cited cultural resistance as a barrier to vaccination. A significant proportion (80.86%) of students had not been vaccinated against HBV. Among vaccinated students, 17.4%, 15.7%, and 47.82% had received one, two, and three doses, respectively. Barriers to vaccination included safety concerns (77.4%), lack of time (86.52%), and doubts about efficacy (42.61%). Conclusion: This study highlights gaps in knowledge and vaccination coverage among medical students, which are critical for their health and future clinical practice. Enhancing awareness and vaccination rates can empower students to advocate for preventative measures in their professional environments. Despite high awareness of HBV, knowledge gaps and cultural barriers persist, affecting attitudes and vaccination uptake among medical students. Educational interventions addressing misconceptions, cultural resistance, and vaccine safety are critical. Increased advocacy for mandatory vaccination policies in healthcare settings is also essential to improve HBV prevention methods.展开更多
基金the National Natural Science Founda-tion of China(62062062)hosted by Gulila Altenbek.
文摘Due to the structural dependencies among concurrent events in the knowledge graph and the substantial amount of sequential correlation information carried by temporally adjacent events,we propose an Independent Recurrent Temporal Graph Convolution Networks(IndRT-GCNets)framework to efficiently and accurately capture event attribute information.The framework models the knowledge graph sequences to learn the evolutionary represen-tations of entities and relations within each period.Firstly,by utilizing the temporal graph convolution module in the evolutionary representation unit,the framework captures the structural dependency relationships within the knowledge graph in each period.Meanwhile,to achieve better event representation and establish effective correlations,an independent recurrent neural network is employed to implement auto-regressive modeling.Furthermore,static attributes of entities in the entity-relation events are constrained andmerged using a static graph constraint to obtain optimal entity representations.Finally,the evolution of entity and relation representations is utilized to predict events in the next subsequent step.On multiple real-world datasets such as Freebase13(FB13),Freebase 15k(FB15K),WordNet11(WN11),WordNet18(WN18),FB15K-237,WN18RR,YAGO3-10,and Nell-995,the results of multiple evaluation indicators show that our proposed IndRT-GCNets framework outperforms most existing models on knowledge reasoning tasks,which validates the effectiveness and robustness.
基金supported in part by the Science and Technology Innovation 2030-“New Generation of Artificial Intelligence”Major Project(No.2021ZD0111000)Henan Provincial Science and Technology Research Project(No.232102211039).
文摘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.
文摘In the international shipping industry, digital intelligence transformation has become essential, with both governments and enterprises actively working to integrate diverse datasets. The domain of maritime and shipping is characterized by a vast array of document types, filled with complex, large-scale, and often chaotic knowledge and relationships. Effectively managing these documents is crucial for developing a Large Language Model (LLM) in the maritime domain, enabling practitioners to access and leverage valuable information. A Knowledge Graph (KG) offers a state-of-the-art solution for enhancing knowledge retrieval, providing more accurate responses and enabling context-aware reasoning. This paper presents a framework for utilizing maritime and shipping documents to construct a knowledge graph using GraphRAG, a hybrid tool combining graph-based retrieval and generation capabilities. The extraction of entities and relationships from these documents and the KG construction process are detailed. Furthermore, the KG is integrated with an LLM to develop a Q&A system, demonstrating that the system significantly improves answer accuracy compared to traditional LLMs. Additionally, the KG construction process is up to 50% faster than conventional LLM-based approaches, underscoring the efficiency of our method. This study provides a promising approach to digital intelligence in shipping, advancing knowledge accessibility and decision-making.
文摘BACKGROUND Breast cancer is one of the most prevalent causes of morbidity and mortality worldwide,presenting an increasing public health challenge,particularly in lowincome and middle-income countries.However,data on the knowledge,attitudes,and preventive practices regarding breast cancer and the associated factors among females in Wollo,Ethiopia,remain limited.AIM To assess the impact of family history(FH)of breast disease on knowledge,attitudes,and breast cancer preventive practices among reproductive-age females.METHODS A community-based cross-sectional study was conducted in May and June 2022 in Northeast Ethiopia and involved 143 reproductive-age females with FH of breast diseases and 209 without such a history.We selected participants using the systematic random sampling technique.We analyzed the data using Statistical Package for Social Science version 25 software,and logistic regression analysis was employed to determine odds ratios for variable associations,with statistical significance set at P<0.05.RESULTS Among participants with FH of breast diseases,the levels of knowledge,attitudes,and preventive practices were found to be 83.9%[95%confidence interval(CI):77.9-89.9],49.0%(95%CI:40.8-57.1),and 74.1%(95%CI:66.9-81.3),respectively.In contrast,among those without FH of breast diseases,these levels were significantly decreased to 10.5%(95%CI:6.4-14.7),32.1%(95%CI:25.7-38.4),and 16.7%(95%CI:11.7-21.8),respectively.This study also indicated that knowledge,attitudes,and preventive practices related to breast cancer are significantly higher among participants with FH of breast diseases compared to those without HF breast diseases.CONCLUSION Educational status,monthly income,and community health insurance were identified as significant factors associated with the levels of knowledge,attitudes,and preventive practices regarding breast cancer among reproductive-age females.
文摘Let F_(1)be the virtual field consisting of one element and(Q,I)a string pair.In this paper,we study the representations of string pairs over the virtual field F_(1).It is proved that an indecomposable F_(1)-representation is either a string representation or a band representation by using the coefficient quivers.It is worth noting that for a given band and a positive integer,there exists a unique band representation up to isomorphism.
基金supported by Natural Science Foundation of Sichuan,China(Grant No.:2024ZDZX0019).
文摘Traditional Chinese medicine(TCM)serves as a treasure trove of ancient knowledge,holding a crucial position in the medical field.However,the exploration of TCM's extensive information has been hindered by challenges related to data standardization,completeness,and accuracy,primarily due to the decen-tralized distribution of TCM resources.To address these issues,we developed a platform for TCM knowledge discovery(TCMKD,https://cbcb.cdutcm.edu.cn/TCMKD/).Seven types of data,including syndromes,formulas,Chinese patent drugs(CPDs),Chinese medicinal materials(CMMs),ingredients,targets,and diseases,were manually proofread and consolidated within TCMKD.To strengthen the integration of TCM with modern medicine,TCMKD employs analytical methods such as TCM data mining,enrichment analysis,and network localization and separation.These tools help elucidate the molecular-level commonalities between TCM and contemporary scientific insights.In addition to its analytical capabilities,a quick question and answer(Q&A)system is also embedded within TCMKD to query the database efficiently,thereby improving the interactivity of the platform.The platform also provides a TCM text annotation tool,offering a simple and efficient method for TCM text mining.Overall,TCMKD not only has the potential to become a pivotal repository for TCM,delving into the pharmaco-logical foundations of TCM treatments,but its flexible embedded tools and algorithms can also be applied to the study of other traditional medical systems,extending beyond just TCM.
基金Deep-time Digital Earth(DDE)Big Science Program(No.GJ-C03-SGF-2025-004)National Natural Science Foundation of China(No.42394063)Sichuan Science and Technology Program(No.2025ZNSFSC0325).
文摘Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.
基金supported by National Natural Science Foundation of China(Nos.62266054 and 62166050)Key Program of Fundamental Research Project of Yunnan Science and Technology Plan(No.202201AS070021)+2 种基金Yunnan Fundamental Research Projects(No.202401AT070122)Yunnan International Joint Research and Development Center of China-Laos-Thailand Educational Digitalization(No.202203AP140006)Scientific Research Foundation of Yunnan Provincial Department of Education(No.2024Y159).
文摘In the field of intelligent education,the integration of artificial intelligence,especially deep learning technologies,has garnered significant attention.Knowledge tracing(KT)plays a pivotal role in this field by predicting students’future performance through the analysis of historical interaction data,thereby assisting educators in evaluating knowledgemastery and tailoring instructional strategies.Traditional knowledge tracingmethods,largely based on Recurrent Neural Networks(RNNs)and Transformer models,primarily focus on capturing long-term interaction patterns in sequential data.However,these models may neglect crucial short-term dynamics and other relevant features.This paper introduces a novel approach to knowledge tracing by leveraging a pure Multilayer Perceptron(MLP)architecture.We proposeMixerKT,a knowledge tracing model based on theHyperMixer framework,which uniquely integrates global and localMixer feature extractors.This architecture enables more effective extraction of both long-terminteraction trends and recent learning behaviors,addressing limitations in currentmodels thatmay overlook these key aspects.Empirical evaluations on twowidely-used datasets,ASSIS Tments2009 and Algebra2005,demonstrate that MixerKT consistently outperforms several state-of-the-art models,including DKT,SAKT,and Separated Self-Attentive Neural Knowledge Tracing(SAINT).Specifically,MixerKT achieves higher prediction accuracy,highlighting its effectiveness in capturing the nuances of learners’knowledge states.These results indicate that our model provides a more comprehensive representation of student learning patterns,enhancing the ability to predict future performance with greater precision.
基金supported by the National Natural Science Foundation of China(Nos.82173746 and U23A20530)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission)。
文摘Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.
基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDB0740000National Key Research and Development Program of China,No.2022YFB3904200,No.2022YFF0711601+1 种基金Key Project of Innovation LREIS,No.PI009National Natural Science Foundation of China,No.42471503。
文摘Deep-time Earth research plays a pivotal role in deciphering the rates,patterns,and mechanisms of Earth's evolutionary processes throughout geological history,providing essential scientific foundations for climate prediction,natural resource exploration,and sustainable planetary stewardship.To advance Deep-time Earth research in the era of big data and artificial intelligence,the International Union of Geological Sciences initiated the“Deeptime Digital Earth International Big Science Program”(DDE)in 2019.At the core of this ambitious program lies the development of geoscience knowledge graphs,serving as a transformative knowledge infrastructure that enables the integration,sharing,mining,and analysis of heterogeneous geoscience big data.The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1)establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2)developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3)implementing a comprehensive three-tiered architecture encompassing basic,discipline-specific,and application-oriented knowledge graphs,spanning approximately 20 geoscience disciplines.Through its open knowledge framework and international collaborative network,this initiative has fostered multinational research collaborations,establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable,Accessible,Interoperable,Reusable)data practices in deep-time Earth systems research.
基金supported by the Shenzhen Key Laboratory of Intelligent Bioinformatics(No.ZDSYS20220422103800001)the Shenzhen Science and Technology Program(No.JCYJ20230807140709020)+2 种基金National Natural Science Foundation of China(Nos.62402489,U22A2041,and 62373172)the China Postdoctoral Science Foundation(No.2023M743688)Guangdong Basic and Applied Basic Research Foundation(Nos.2024A1515011960 and 2023A1515110570)。
文摘Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable,thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making.This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations.The research methodology begins with the compilation of small molecule databases,followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms,capturing patterns and salient features across extensive chemical spaces.The study then examines various drug discovery downstream tasks,including drug-target interaction(DTI)prediction,drug-target affinity(DTA)prediction,drug property(DP)prediction,and drug generation,all based on learned representations.The analysis concludes by highlighting challenges and opportunities associated with machine learning(ML)methods for molecular representation and improving downstream task performance.Additionally,the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine(TCM)medicinal substances and facilitating TCM target discovery.
文摘Xi Jinping,general secretary of the Communist Party of China(CPC)Central Committee,stressed that we should adhere to the“two integrations”(namely,integrating the basic tenets of Marxism with China’s specific realities and fine traditional culture),root ourselves in Chinese soil,carry forward the Chinese cultural heritage,and strengthen the academic foundation.We should accelerate the building of an independent knowledge system for Chinese philosophy and social sciences,and formulate original concepts and develop systems of academic discipline,research and discourse,drawing on China’s rich experience of advancing human rights.In the face of changes of a magnitude not seen in a century,in the historic process of advancing the great rejuvenation of the Chinese nation on all fronts through Chinese modernization,we should and must strengthen our theoretical self-consciousness and confidence in the path of Chinese modernization.We need to enhance human rights research,develop the human rights theoretical system and paradigm that are based on Chinese realities and express Chinese voice,and an independent Chinese knowledge system for human rights.
文摘Background: Blood transfusion (BT) is crucial to the provision of modern health care. However, blood is scarce and costly, and its use is associated with risks. Therefore, the medical professionals who handle it should have adequate knowledge to ensure rational and safe utilization. The objective of the study was to determine the level of BT knowledge among junior medical doctors in Kenya. Methodology: A cross-sectional study was conducted among junior medical doctors working in Western Kenya. Data was collected using questionnaires from August 2021 to March 2022, and analysis was done by way of descriptive and inferential statistics. A p Results: A total of 150 medical doctors participated in the study. Males comprised 60% (n = 90), and the mean age of the participants was 29.9 (SD 3.6) with a range of 25 - 45 years. The mean knowledge score was 54.1% ± 16.4% and was associated with orientation (AOR = 3.157, 95% CI = 1.194 - 8.337). Conclusion: Blood transfusion knowledge among the doctors was suboptimal and was associated with pre-internship induction. There is a need for additional education in BT during all phases of medical training and practice, including orientation for medical interns.
文摘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.
文摘The integration of digital tools and effective knowledge management practices is critical for enhancing administrative efficiency and institutional continuity in higher education. This study investigates the relationships between knowledge modeling, institutional memory, leadership styles, technology, and administrative efficiency at the University of Cape Coast (UCC). The study sought to identify the challenges and opportunities in integrating digital tools into administrative processes and to provide actionable recommendations for improvement. A mixed-methods research design was employed, combining quantitative analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) with qualitative thematic analysis of interviews. The findings revealed key challenges, including resistance to change, fragmented knowledge repositories, and inadequate funding, alongside opportunities such as centralized knowledge systems, cost-effective open-source tools, and capacity-building initiatives. The study highlights the importance of strategic leadership, robust policies, and investments in digital infrastructure to enhance administrative practices. Policy implications include the need for clear digital transformation guidelines and leadership training to foster innovation and collaboration. Recommendations include investing in scalable digital tools, implementing comprehensive capacity-building programs, and promoting stakeholder engagement to drive successful digital integration. These insights provide a roadmap for UCC and similar institutions seeking to optimize administrative efficiency through digital transformation.
文摘Introduction: Breastfeeding is the best way to provide ideal nutrition for optimal infant growth and development. Objectives: The aim of our work was to assess the knowledge, attitudes and practices of mothers of children aged 0 - 24 months on exclusive breastfeeding in the Central African Republic. Methods: This was a cross-sectional, descriptive and analytical study conducted from September 15 to October 15, 2024 among mothers of infants aged 0 to 24 months. Sociodemographic, obstetric and breastfeeding-related data were collected through individual interviews conducted during sensitizations on good feeding practices organized by the Tina Touadera Foundation. The chi2 test was used to test for relationships between variables, and the p significance level was set at 0.05. Results: The average age of the mothers surveyed was 27.67 years. 65.69% (n = 247) of mothers lived in urban areas and 55.85% (n = 210) were Muslim. 56.38% (n = 212) were living common-law and 34.04% (n = 128) were poor. Secondary-school mothers (44.42%, n = 167) and housewives (53.72%, n = 202) were in the majority. Exclusive breastfeeding (EBF) was correctly defined by 79.26% (n = 298) of mothers and actually practised in 24.20% (n = 91) of cases. The main source of information was a health professional in 75.36% (n = 304) of cases. Among the 285 mothers who practised mixed breastfeeding, lack of time (33.33%) was the main reason. They acknowledged having given water (100%), corn porridge (75.09%) and/or artificial milk (24.91%) before the first 6 months of life. The average time for introducing water was 2.2 months, and for porridge/formula 2.79 months. More than half the mothers (55.05%) said they did not know their infants’ weaning age. Factors positively influencing the use of EBF were age under 29, residence in an urban area, primiparity, having been informed about AME by a health professional, and being a housewife or shopkeeper (p Conclusion: Mothers’ level of knowledge was heterogeneous but insufficient overall. An effective system of information and education from pregnancy to the first six months of life is needed to promote breastfeeding.
基金supported by the National Science and Technology Council(NSTC),Taiwan,under Grants Numbers 112-2622-E-029-009 and 112-2221-E-029-019.
文摘In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.
文摘Introduction: Uterine fibroids are benign tumors that develop from the connective and muscular tissues of the uterus. Common among African-American women, patients suffering from them often arrive late to the hospital in our African regions. This study aimed to investigate the knowledge and perception of uterine fibroids among women who came to the gynecology-obstetrics department of the Regional Hospital Center (CHR) Tsévié. Methodology: It was a cross-sectional descriptive study, with data collection conducted from May 7th to 20th, 2024, using systematic sampling. The study included all women present in the Gynecology-Obstetrics Department of CHR Tsévié during the study period who willingly and informedly consented to participate in the survey. Results: 362 women participated in the study. Among them, 36.8% had a secondary level, and 72.9% were Christians. About 97.5% had heard of uterine fibroids. In 63.5% of cases, their entourage was the principal source of information. The diagnostic methods mentioned by the women were ultrasound in 94.6% of cases, while prayers and occultism were also cited in 28% and 33.3% of cases, respectively. While 91.9% of the women considered the hospital, the place for treatment, some indicated that treatment would require plant-based approaches (46.8%) and prayers (26%). The cost of treatment was an obstacle for 85.4% of women, and 61.3% expressed fear of dying during surgery. There was a statistically significant relationship between treatment choice and religion. Conclusion: The majority of women had heard of uterine fibroids but had incorrect information about the treatment.
文摘Non-communicable diseases (NCDs) are on the rise worldwide and in developing countries like Botswana. Unhealthy eating habits and lack of proper nutrition knowledge cause non-communicable diseases and affect adolescents. It is in adolescence that eating habits are formed that persist till adulthood. Lifestyle interventions are needed to curb NCDs in adolescents. This paper reports the findings of a study that aimed to validate a lifestyle intervention program and its effect on food intake, physical activity, and nutrition knowledge. It was a clustered randomized control trial study conducted in four (4) junior secondary schools. There were 46 participants, 21 in the control and 25 in the intervention arm, who were blindly assigned to each arm by a statistician. Information and skills on nutrition were imparted using the Information, Motivation, and Behavioral Skills model. The program was implemented for eight (8) weeks hourly after school. A questionnaire was used to collect data pre- and post-intervention. Number, proportion, percentage, and independent t-test (mean and SD or median and IQR, p-value) were calculated using numerical and categorical data. The findings showed that the lifestyle intervention was valid, and there was a slight decrease in the intake of sweets among participants in both trial arms (p = 0.066). There was no significant difference in terms of food intake. Only a small number of participants still ate a few fruits, and there was no change in vegetable intake in both trial arms (p = 0.641). There was no change in the intake of fried foods in both trail arms (p = 0.402). Regarding nutrition knowledge, there was a slight significant difference of p = 0.079 between the trial arms. Though the effect of the lifestyle intervention program was not statistically significant, the results are promising, especially if the duration could be increased to a longer period and a larger sample size included.
文摘Background: Hepatitis B virus (HBV) is a primary reason for liver cancer and continues to be a worldwide public health issue. The likelihood of contracting HBV is greater in healthcare workers (HCWs) compared to individuals who are not in healthcare professions. Medical students are classified as a high-risk demographic since, like HCWs, they often come into contact with bodily fluids and blood during their clinical training. By 2030, a greater proportion of people will have received HBV vaccinations, thereby halting the spread of new infections—The Somali Ministry of Health with the help of various agencies announced to eradicate hepatitis from Somalia. The priority actions are national hepatitis strategy, hepatitis survey, public awareness, training, and capacity building. Objectives: This study aims to assess the knowledge, attitude, and vaccination status of Hepatitis B infection among medical university students in Mogadishu, Somalia, 2024. Methods: Cross-sectional study design was used in this study and the survey was carried out among medical students enrolled in Universities from April 1, 2023 to June 30, 2023. The data was analyzed using SPSS version 26.0 software, Chi-square analysis and Logistic regression analysis to identify associations between demographic factors and HBV knowledge, attitudes, and vaccination status, as well as perspectives and immunization status concerning viral hepatitis. Results: The study achieved a response rate of (96%), with 230 participants. Most students (76.5%) were aged 26 - 30 years, and (60.8%) were male. Nearly half (48.7%) were in their third year of study, and the majority (36.1%) were from the Medicine and Surgery department. While 92.2% had heard of HBV, gaps in understanding were evident. About 37.8% erroneously believed HBV could spread via handshakes, and only 33.9% were aware HBV is treatable. Awareness of HBV’s severe complications, such as liver cirrhosis and liver cancer, was reported by 61.3%, and 83% understood that vaccination could prevent infection. Positive attitudes towards HBV vaccination were prevalent. Most participants (81.3%) supported vaccination before sexual activity, and 78.3% endorsed mandatory HBV vaccination policies for healthcare workers. However, 87.4% expressed concerns about the vaccine promoting unsafe sexual behavior, and 96.1% cited cultural resistance as a barrier to vaccination. A significant proportion (80.86%) of students had not been vaccinated against HBV. Among vaccinated students, 17.4%, 15.7%, and 47.82% had received one, two, and three doses, respectively. Barriers to vaccination included safety concerns (77.4%), lack of time (86.52%), and doubts about efficacy (42.61%). Conclusion: This study highlights gaps in knowledge and vaccination coverage among medical students, which are critical for their health and future clinical practice. Enhancing awareness and vaccination rates can empower students to advocate for preventative measures in their professional environments. Despite high awareness of HBV, knowledge gaps and cultural barriers persist, affecting attitudes and vaccination uptake among medical students. Educational interventions addressing misconceptions, cultural resistance, and vaccine safety are critical. Increased advocacy for mandatory vaccination policies in healthcare settings is also essential to improve HBV prevention methods.
