This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electroca...This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.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
As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate...As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS)is a common infectious disease in the global swine industry that causes economically great losses.Traditional Chinese medicine(TCM)has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective approaches.Here,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of TCMs.Subsequently,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e.,transR)from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@10.Based on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of PRRS.Ultimately,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90%when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/mL.In summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.展开更多
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 extr...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.展开更多
A knowledge graph(KG)is a knowledge base that integrates and represents data based on a graph-structured data model or topology.Geoscientists have made efforts to construct geosciencerelated KGs to overcome semantic h...A knowledge graph(KG)is a knowledge base that integrates and represents data based on a graph-structured data model or topology.Geoscientists have made efforts to construct geosciencerelated KGs to overcome semantic heterogeneity and facilitate knowledge representation,data integration,and text analysis.However,there is currently no comprehensive paleontology KG or data-driven discovery based on it.In this study,we constructed a two-layer model to represent the ordinal hierarchical structure of the paleontology KG following a top-down construction process.An ontology containing 19365 concepts has been defined up to 2023.On this basis,we derived the synonymy list based on the paleontology KG and designed corresponding online functions in the OneStratigraphy database to showcase the use of the KG in paleontological research.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
Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the...Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.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
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
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
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
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
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
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
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
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
The article is based on language model,through the cue word engineering and agent thinking method,automatic knowledge extraction,with China accounting standards support to complete the corresponding knowledge map cons...The article is based on language model,through the cue word engineering and agent thinking method,automatic knowledge extraction,with China accounting standards support to complete the corresponding knowledge map construction.Through the way of extracting the accounting entities and their connections in the pattern layer,the data layer is provided for the fine-tuning and optimization of the large model.Studies found that,through the reasonable application of language model,knowledge can be realized in massive financial data neural five effective extracted tuples,and complete accounting knowledge map construction.展开更多
文摘This review presents a comprehensive and forward-looking analysis of how Large Language Models(LLMs)are transforming knowledge discovery in the rational design of advancedmicro/nano electrocatalyst materials.Electrocatalysis is central to sustainable energy and environmental technologies,but traditional catalyst discovery is often hindered by high complexity,fragmented knowledge,and inefficiencies.LLMs,particularly those based on Transformer architectures,offer unprecedented capabilities in extracting,synthesizing,and generating scientific knowledge from vast unstructured textual corpora.This work provides the first structured synthesis of how LLMs have been leveraged across various electrocatalysis tasks,including automated information extraction from literature,text-based property prediction,hypothesis generation,synthesis planning,and knowledge graph construction.We comparatively analyze leading LLMs and domain-specific frameworks(e.g.,CatBERTa,CataLM,CatGPT)in terms of methodology,application scope,performance metrics,and limitations.Through curated case studies across key electrocatalytic reactions—HER,OER,ORR,and CO_(2)RR—we highlight emerging trends such as the growing use of embedding-based prediction,retrieval-augmented generation,and fine-tuned scientific LLMs.The review also identifies persistent challenges,including data heterogeneity,hallucination risks,lack of standard benchmarks,and limited multimodal integration.Importantly,we articulate future research directions,such as the development of multimodal and physics-informedMatSci-LLMs,enhanced interpretability tools,and the integration of LLMswith selfdriving laboratories for autonomous discovery.By consolidating fragmented advances and outlining a unified research roadmap,this review provides valuable guidance for both materials scientists and AI practitioners seeking to accelerate catalyst innovation through large language model technologies.
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supported by the China Fundamental Research Funds for the Central Universities(No.2662022XXYJ001,2662022JC004,2662023XXPY005)。
文摘As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS)is a common infectious disease in the global swine industry that causes economically great losses.Traditional Chinese medicine(TCM)has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective approaches.Here,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of TCMs.Subsequently,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e.,transR)from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@10.Based on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of PRRS.Ultimately,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90%when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/mL.In summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.
基金supported by the National Natural Science Foundation of China(Grant Nos.92152102 and 92152202)the Advanced Jet Propulsion Innovation Center/AEAC(Grant No.HKCX2022-01-010)。
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.41725007,42250104,41830323,42002015,and 42302001)the Fundamental Research Funds for the Central Universities(Nos.020614380168,JZ2023HGQA0144 and JZ2023HGTA0175)。
文摘A knowledge graph(KG)is a knowledge base that integrates and represents data based on a graph-structured data model or topology.Geoscientists have made efforts to construct geosciencerelated KGs to overcome semantic heterogeneity and facilitate knowledge representation,data integration,and text analysis.However,there is currently no comprehensive paleontology KG or data-driven discovery based on it.In this study,we constructed a two-layer model to represent the ordinal hierarchical structure of the paleontology KG following a top-down construction process.An ontology containing 19365 concepts has been defined up to 2023.On this basis,we derived the synonymy list based on the paleontology KG and designed corresponding online functions in the OneStratigraphy database to showcase the use of the KG in paleontological research.
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.
基金supported in part by National Institute of Health(NIH),USA(Grant Nos.:R01GM126189,R01AI164266,and R35GM148196)the National Science Foundation,USA(Grant Nos.DMS2052983,DMS-1761320,and IIS-1900473)+3 种基金National Aero-nautics and Space Administration(NASA),USA(Grant No.:80NSSC21M0023)Michigan State University(MSU)Foundation,USA,Bristol-Myers Squibb(Grant No.:65109)USA,and Pfizer,USAsupported by the National Natural Science Foundation of China(Grant Nos.:11971367,12271416,and 11972266).
文摘Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.
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.
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.
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.
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.
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The article is based on language model,through the cue word engineering and agent thinking method,automatic knowledge extraction,with China accounting standards support to complete the corresponding knowledge map construction.Through the way of extracting the accounting entities and their connections in the pattern layer,the data layer is provided for the fine-tuning and optimization of the large model.Studies found that,through the reasonable application of language model,knowledge can be realized in massive financial data neural five effective extracted tuples,and complete accounting knowledge map construction.
