Heterogeneous Image Knowledge Driven Visual Perception

Dear Editor,This letter is concerned with visual perception closely related to heterogeneous images.Facing the huge challenge brought by different image modalities,we propose a visual perception framework based on heterogeneous image knowledge,i.e.,the domain knowledge associated with specific vision tasks,to better address the corresponding visual perception problems.

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client data.To enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

A new evolutional model for institutional field knowledge flow network

Purpose:This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model(IKM).The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.Design/Methodology/Approach:The IKM model enhances the preferential attachment and growth observed in scale-free BA networks,while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network.To compare its performance,the BA and DMS models are also employed for simulating the network.Pearson coefficient analysis is conducted on the simulated networks generated by the IKM,BA and DMS models,as well as on the actual network.Findings:The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network.It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm.The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.Research Limitations:This study has some limitations.Firstly,it primarily focuses on the evolution of knowledge flow networks within the field of physics,neglecting other fields.Additionally,the analysis is based on a specific set of data,which may limit the generalizability of the findings.Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.Practical Implications:The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions.It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations.The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.Originality/value:This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks.The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions.Moreover,the model has the potential to be applied to other knowledge networks,which are formed by knowledge organizations as node units.

A Temporal Knowledge Graph Embedding Model Based on Variable Translation

Knowledge representation learning(KRL)aims to encode entities and relationships in various knowledge graphs into low-dimensional continuous vectors.It is popularly used in knowledge graph completion(or link prediction)tasks.Translation-based knowledge representation learning methods perform well in knowledge graph completion(KGC).However,the translation principles adopted by these methods are too strict and cannot model complex entities and relationships(i.e.,N-1,1-N,and N-N)well.Besides,these traditional translation principles are primarily used in static knowledge graphs and overlook the temporal properties of triplet facts.Therefore,we propose a temporal knowledge graph embedding model based on variable translation(TKGE-VT).The model proposes a new variable translation principle,which enables flexible transformation between entities and relationship embedding.Meanwhile,this paper considers the temporal properties of both entities and relationships and applies the proposed principle of variable translation to temporal knowledge graphs.We conduct link prediction and triplet classification experiments on four benchmark datasets:WN11,WN18,FB13,and FB15K.Our model outperforms baseline models on multiple evaluation metrics according to the experimental results.

Nutrition Awareness and Knowledge and Body Mass Index in Adolescents in Public Junior Secondary Schools

Background: Non-communicable diseases are increasing among adolescents. The decision about diet is the foundation of eating habits that could persist to adulthood. Dietary decisions, which usually are hard to change later in life, make nutrition education at school paramount to prevent obesity and NCDs, and promote healthy eating. Objectives: To assess level of nutrition awareness and knowledge of healthy eating and food intake behaviors and association with Body Mass Index (BMI) and age. Methods: A cross sectional study that included measures such as age, gender, socioeconomic status, BMI, and nutrition knowledge was conducted in 264 respondents from 18th June 2015 to 9th July 2015. The nutrition knowledge questionnaire was composed of 24 questions divided into food nutrients, food contents, healthiest foods, and energy expenditure and nutrition benefits. CDC BMI chart for 2-20-year-olds was used to plot respondent’s weight and height. Results: The mean age of the respondents was 14.3 years (SD 0.79). Most of the respondents (153/252, 53.6%) had a low socio-economic status as categorized by the present study. The mean (SD) BMI was 20.08 (3.90). Most respondents (56.3%, 142/252) failed the knowledge test and scored below 50% and only two respondents (0.8%) had excellent nutrition knowledge. The mean percentage achieved was 46.1% (SD 15.91) ranging from 8.3% to 87.5%. There was a significant correlation between nutrition knowledge and BMI (p = 0.001). Conclusion: The study shows that adolescents do not have adequate nutrition knowledge, placing them at risk for developing non-communicable diseases later in life. Nutrition education programs are urgently needed for teachers, parents, and children.

GATiT:An Intelligent Diagnosis Model Based on Graph Attention Network Incorporating Text Representation in Knowledge Reasoning

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.

Combining Deep Learning with Knowledge Graph for Design Knowledge Acquisition in Conceptual Product Design

The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design.This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph.Specifically,the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data,and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design.Moreover,the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module,and uses multi-granularity information to overcome segmentation errors and polysemy ambiguity in the relation extraction module.Experimental comparison verified the effectiveness and accuracy of the proposed knowledge extraction model.The case study demonstrated the feasibility of the knowledge graph construction with real fragmentary porcelain data and showed the capability to provide designers with interconnected and visualised design knowledge.

A Survey of Knowledge Graph Construction Using Machine Learning

Knowledge graph(KG)serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework.This framework facilitates a transformation in information retrieval,transitioning it from mere string matching to far more sophisticated entity matching.In this transformative process,the advancement of artificial intelligence and intelligent information services is invigorated.Meanwhile,the role ofmachine learningmethod in the construction of KG is important,and these techniques have already achieved initial success.This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning.With a profound amalgamation of cutting-edge research in machine learning,this article undertakes a systematical exploration of KG construction methods in three distinct phases:entity learning,ontology learning,and knowledge reasoning.Especially,a meticulous dissection of machine learningdriven algorithms is conducted,spotlighting their contributions to critical facets such as entity extraction,relation extraction,entity linking,and link prediction.Moreover,this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled,large-scale KG construction.

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

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

Reviewer Acknowledgements

The editors of International Journal of Ophthalmology gratefully acknowledge the members of IJO Editorial Board and reviewers from 57 countries and regions who participated in the peer-reviews and provided their valuable comments between Nov.1^(st),2022 and Oct.31^(st),2023.

Assessment of Knowledge, Attitude and Practices among Healthcare Workers in a Tertiary Care Hospital on Needle Stick Injury

Purpose: Needle-stick injury (NSI) is one of the most potential occupational hazards for healthcare workers because of the transmission of blood-borne pathogens. As per recent data, around 30 lakh healthcare workers sustain Needle stick injuries each year. This study was conducted to assess healthcare workers’ knowledge, attitude and practices regarding needle stick injury. Materials & Methods: A cross-sectional study was conducted in a Tertiary Care Hospital over the period of 3 months. The study population consisted of Intern Doctors, Post Graduate resident Doctors, Staff Nurses, laboratory technicians of Government Medical College and New Civil Hospital, Surat (n = 300). The data were collected using a self-administered questionnaire via the means of Google Forms. Questionnaire was made with prior review literature. The data obtained were entered and analysed in Microsoft Excel. Results: The prevalence of NSI in our study was 46%, with a higher prevalence among the PG residents (72%). Overall scores regarding knowledge and attitude were better in PG residents (knowledge score > 7 in 71% and Attitude Score > 7 in 68% of PG Residents). Even though the PG residents scored highest in the knowledge category, the majority of them suffered needle stick injuries as a result of poor practice scores. Among those who had NSI (n = 139/300), 70% of study participants had superficial injuries, only 9% reported the incident, 18% got medical attention within 2 hours of the incident, and 7% followed up to recheck their viral markers status. Most incidents of NSI were due to hypodermic needles while recapping needles. Conclusion: Exposure to needle stick injuries and their underreporting remains a common problem. It is imperative that healthcare workers receive regular training on the proper handling of sharp objects. We can also draw the conclusion that preventing NSIs requires putting knowledge into practice.

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

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

Investigation on the Knowledge, Attitude and Practice of Oral Health among Medical College and University Undergraduate

Objectives: This study aims to investigate the status of knowledge, attitude and practice (KAP) of oral health among medical undergraduate students, and provide reference for implementing oral health interventions. Methods: A total of 528 undergraduate students enrolled in Fuzhou Medical College from February 2023 to September 2023 were selected as the research subjects. Their oral health KAP were investigated, and the oral health behavior habits of different types of medical students were compared, and possible influencing factors were analyzed. Results: The total awareness rate of oral health knowledge among medical students is 77.0%, with an average score of 3.85 ± 1.16 points. The overall positive rate of oral health attitudes among medical students is 80.0%, with an average score of 3.19 ± 0.72 points. The total qualified rate of oral health behavior is 65.9%, with an average score of 4.61 ± 1.23 points. The scores of oral health knowledge, attitudes, and behaviors among medical students are related to gender, major, smoking status, and oral health status. The frequency of brushing teeth in the female group was higher than that in the male group, while the habit of brushing teeth before bedtime and the frequency of timely replacement of toothbrushes when deformed were lower, with statistical significance (p 0.05). The frequency of timely replacement of toothbrushes varies among medical students from different majors, and the difference is statistically significant (p 0.05). People who have a habit of eating hot and cold food have a higher frequency of brushing their teeth every day, and the difference is statistically significant (p 0.05). Non smokers have a better habit of brushing their teeth before bedtime and a higher frequency of timely replacement when their toothbrush deforms, with a statistically significant difference (p 0.05). The frequency of using fluoride toothpaste or medicated toothpaste, having a habit of unilateral chewing, and timely replacement of toothbrushes when deformed in patients with existing oral problems is higher than that of those without oral problems, and the difference is statistically significant (p 0.05). Conclusion: The knowledge, attitude, and behavior of oral health among medical students in this school are above average. Students with different genders, dietary and smoking habits, and oral health status have different oral health behavioral habits. It is recommended to include oral health education in mandatory courses for various medical majors.

Knowledge of Hospital Acquired Infections (HAIs) among Medical Students in a Tertiary Hospital in Jos North Local Government Area, Plateau State, Nigeria

Background: Hospital Acquired Infections (HAIs) remain a common cause of death, functional disability, emotional suffering and economic burden among hospitalized patients. Knowledge of HAIs is important in its prevention and control. This study seeks to assess the knowledge of Hospital Acquired Infections (HAIs) among medical students in a Tertiary Hospital in Jos North Local Government Area, Plateau State, Nigeria. Methods: This was a descriptive cross-sectional study done in October 2019 among clinical medical students using a Multistage sampling technique. Data was collected using a self-administered structured questionnaire and analyzed using the IBM SPSS 20 (Statistical Package for the Social Sciences). Ethical approval was granted by Bingham University Teaching Hospital, Ethics Committee, Jos, Plateau State. Results: A total of 219 students in the clinical arm of the College of Medicine and Health Sciences were selected. A higher proportion (97.7%) of respondents knew about Hospital Acquired Infections and 85.4% knew that Hospital Acquired infections occur in the hospital, and (86.3%) considered patients contagious with half (58.9%) considered patients as the most important source of HAIs, followed by care givers (13.2%), then doctors including medical students and interns (10.0%) and lastly nurses (8.7%). The majority of respondents (70.8%) considered Surgical Wound Infections to be the most commonly occurring HAI, followed by UTIs (69.9%), RTIs (61.2%), BSIs (37.0%) and others (0.9%). The clinical thermometer was the instrument that most commonly transmits HAIs (82.6%), then followed by stethoscope (62.1%), white coats (53.9%), and blood pressure cuff (51.1%). Most respondents knew the infectious substances, like blood (96.3%), nasal discharge (82.6%), saliva (85.3%), and faeces (79.4%) transmitted HAIs, 72.6% of the respondents said that they were aware of the recommended hand washing techniques by WHO. Conclusion: The majority of students 91.3% had good knowledge while 8.7% had poor knowledge of HAIs. Lower classes had more respondents with poor knowledge. This finding was statistically significant (p = 0.002, Chi-square 12.819). Students are encouraged to keep up the level of knowledge they have about HAIs. These students can help improve the knowledge of those whose knowledge level is low. Government and NGOs should support sponsorship for capacity-building events targeted at HAIs for healthcare workers and medical students.

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

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

RepDNet:A re-parameterization despeckling network for autonomous underwater side-scan sonar imaging with prior-knowledge customized convolution

Side-scan sonar(SSS)is now a prevalent instrument for large-scale seafloor topography measurements,deployable on an autonomous underwater vehicle(AUV)to execute fully automated underwater acoustic scanning imaging along a predetermined trajectory.However,SSS images often suffer from speckle noise caused by mutual interference between echoes,and limited AUV computational resources further hinder noise suppression.Existing approaches for SSS image processing and speckle noise reduction rely heavily on complex network structures and fail to combine the benefits of deep learning and domain knowledge.To address the problem,Rep DNet,a novel and effective despeckling convolutional neural network is proposed.Rep DNet introduces two re-parameterized blocks:the Pixel Smoothing Block(PSB)and Edge Enhancement Block(EEB),preserving edge information while attenuating speckle noise.During training,PSB and EEB manifest as double-layered multi-branch structures,integrating first-order and secondorder derivatives and smoothing functions.During inference,the branches are re-parameterized into a 3×3 convolution,enabling efficient inference without sacrificing accuracy.Rep DNet comprises three computational operations:3×3 convolution,element-wise summation and Rectified Linear Unit activation.Evaluations on benchmark datasets,a real SSS dataset and Data collected at Lake Mulan aestablish Rep DNet as a well-balanced network,meeting the AUV computational constraints in terms of performance and latency.

De-biased knowledge distillation framework based on knowledge infusion and label de-biasing techniques

Knowledge distillation,as a pivotal technique in the field of model compression,has been widely applied across various domains.However,the problem of student model performance being limited due to inherent biases in the teacher model during the distillation process still persists.To address the inherent biases in knowledge distillation,we propose a de-biased knowledge distillation framework tailored for binary classification tasks.For the pre-trained teacher model,biases in the soft labels are mitigated through knowledge infusion and label de-biasing techniques.Based on this,a de-biased distillation loss is introduced,allowing the de-biased labels to replace the soft labels as the fitting target for the student model.This approach enables the student model to learn from the corrected model information,achieving high-performance deployment on lightweight student models.Experiments conducted on multiple real-world datasets demonstrate that deep learning models compressed under the de-biased knowledge distillation framework significantly outperform traditional response-based and feature-based knowledge distillation models across various evaluation metrics,highlighting the effectiveness and superiority of the de-biased knowledge distillation framework in model compression.

Rabies by Dog Bites in Benin: Determinants of Good Knowledge of the Rabies Risk among Subjects Exposed in the Plateau Department in 2023

Rabies, a viral zoonosis, is one of the neglected tropical diseases (NTDs) that mainly affects poor and vulnerable populations. Human rabies is endemic in Benin. Although listed among the notifiable diseases, it nevertheless remains a neglected and under-reported disease. The objective of this study was to investigate the determinants of knowledge of rabies risk among subjects exposed to human rabies by dog bites in the Plateau department of Benin. Methods: This was a cross-sectional study with an analytical aim which involved a sample of 900 people aged at least 18 years old chosen randomly and residing in the Plateau department for at least six months. A pre-tested and validated digital questionnaire served as a data collection tool. Data analysis was done with R 3.4.1 software. The prevalence ratio was used as a measure of association. The confidence threshold was set at 5%. We carried out a multiple logistic regression using the likelihood ratio (top-down approach) in order to identify the main factors which influenced the good knowledge of human rabies in the population of Plateau. Results: In the study population, 80.89% of the subjects surveyed lived in a rural area. The sex ratio (Male/Female) was 1.11. The average age of the subjects was 40.24 ± 14.52 years. The prevalence of dog bites was 4.11% (95% CI = [3.00;5.63]) or 37 subjects bitten. After evaluation of knowledge of rabies risk in the study population, the main factors which positively influenced good knowledge of human rabies were age (p linear trend 0.01), residence of the subject in the municipality of Pobè (ORa = 8.34;95% CI = 0.83 - 1.23;p < 0.01), individual dwelling (ORa = 17.95;95% CI = 13.12 - 49.58;p < 0. 01), educational level of at least secondary (ORa = 5.53;CI 95 = 3.06 - 10.15;p = 0.01), the existence of a veterinary center (ORa = 3.66;CI 95 = 1.07 - 12.43 p = 0.01) and knowledge of a dog bites victim (ORa = 6.55;95% CI = 2.73 - 8.25;p < 0.01). Conclusion: This population study confirms that human rabies remains a public health problem with a need to raise awareness among populations on good prevention practices against human rabies after a dog bite in the Plateau department. This could help to improve strategies surveillance for the control and eradication of human rabies through the implementation of the One Health approach.

Domain Knowledge Used in Meta-Heuristic Algorithms for the Job-Shop Scheduling Problem:Review and Analysis

Meta-heuristic algorithms search the problem solution space to obtain a satisfactory solution within a reasonable timeframe.By combining domain knowledge of the specific optimization problem,the search efficiency and quality of meta-heuristic algorithms can be significantly improved,making it crucial to identify and summarize domain knowledge within the problem.In this paper,we summarize and analyze domain knowledge that can be applied to meta-heuristic algorithms in the job-shop scheduling problem(JSP).Firstly,this paper delves into the importance of domain knowledge in optimization algorithm design.After that,the development of different methods for the JSP are reviewed,and the domain knowledge in it for meta-heuristic algorithms is summarized and classified.Applications of this domain knowledge are analyzed,showing it is indispensable in ensuring the optimization performance of meta-heuristic algorithms.Finally,this paper analyzes the relationship among domain knowledge,optimization problems,and optimization algorithms,and points out the shortcomings of the existing research and puts forward research prospects.This paper comprehensively summarizes the domain knowledge in the JSP,and discusses the relationship between the optimization problems,optimization algorithms and domain knowledge,which provides a research direction for the metaheuristic algorithm design for solving the JSP in the future.

Study of Factors Associated with Knowledge, Attitudes and Practices Regarding Rabies in the Commune of Niakhéne in Senegal, in 2022

Introduction: Rabies is a serious disease, as it is always fatal, but it can be prevented by sero-vaccination. It is a neglected tropical disease endemic in Asia and Africa. The aim of this study was to assess knowledge, attitudes and practices regarding rabies and to determine the factors associated with them among people aged 18 and over in the commune of Niakhène. Methods: This was a cross-sectional, descriptive and analytical survey of subjects aged 18 and over living in the commune of Niakhène. A sample of 300 individuals was drawn from a two-stage cluster survey stratified by age and sex. Bivariate analysis was performed using association tests. Results: The mean age of respondents was 35.3 ± 16.9 years. It was noted that 67% (201) of respondents had a good knowledge of rabies. The results showed that 7.3% (22) of respondents owned a dog. Of the 278 people who did not own a dog, 78.4% (218) said they would have vaccinated their dog if they had had one. It should be noted that 83.7% (251) of respondents said they would go to a health facility if an animal bit them. None of the dog owners had vaccinated their dogs against rabies. Of the 41 people exposed to rabies, 39% went to a health facility. The age and education of the respondents had statistically significant associations with knowledge of rabies. Respondents’ age and education were statistically significantly related to whether they had vaccinated a domestic dog. The age, education and economic well-being quintile of respondents’ households had statistically significant associations with the use of a health facility in the event of being bitten or scratched by an animal vector. The education of respondents who had been bitten by an animal vector was statistically significantly associated with the use of a health facility. Conclusion: It would be imperative for human and animal health authorities to collaborate in a “One Health” approach in order to increase knowledge and promote the adoption of good practices in rabies prevention.