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 ...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.展开更多
As a cultural concept refl ecting the relationship between humans and forests,forest culture plays an active role in sustainable forest management.Forest parks provide a wide range of ecosystem services essential for ...As a cultural concept refl ecting the relationship between humans and forests,forest culture plays an active role in sustainable forest management.Forest parks provide a wide range of ecosystem services essential for the sustainable development of society,and the relationships between forest culture,green construction and management of forest parks have practical signifi cance.This study aimed to understand the interaction and process of forest culture infl uencing green construction and management in forest parks with the models Knowledge-Attitude-Practice(KAP)and Theory of Planned Behavior(TPB)by proposing a theoretical model.Four hypotheses were tested using data collected from 193 forest park employees in Heilongjiang Province,China.Our results show that forest culture had a signifi cant infl uence on green construction and forest management.In addition,subjective norm and perceived behavioral control directly impacted behavior in green construction and management of the forest park,whereas attitude did not have an impact.Subjective norm had a direct eff ect on attitude.Results between constructs show that forest culture had an indirect eff ect on planning and construction,and on ecological and economic management.Consequently,it supported three of four hypotheses within the proposed model in determining the infl uence of forest culture on green construction and management.展开更多
Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management.Existing research focuses on the constr...Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management.Existing research focuses on the construction of knowledge management systems and knowledge sharing and transfer mechanisms.With the rapid development and application of cloud computing and big data technology,knowledge management is faced with many problems,such as how to combine with the new generation of information technology,how to achieve integration with organizational business processes,and so on.To solve such problems,this paper proposes a reciprocal collaborative knowledge management model(RCKMmodel)based on cloud computing technology,reciprocity theory,and collaboration technology.RCKM model includes project group management and cloud computing technology,which can realize management,finance,communication,and quality assurance of multiple projects and solve the problem of business integration with knowledge management.This paper designs evaluation methods of tacit knowledge and reciprocity preference based on the Bayesian formula and analyzes their effect with simulation data.The methods can provide quantitative support for the integration of knowledge management and business management to realize reciprocity and collaboration in the RCKM model.The research found that RCKM model can fully use cloud computing technology to promote the integration of knowledge management and organizational business,and the evaluation method based on the Bayesian formula can provide relatively accurate data support for the evaluation and selection of project team members.展开更多
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 het...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.展开更多
Equipment defect detection is essential to the security and stabil-ity of power grid networking operations.Besides the status of the power grid itself,environmental information is also necessary for equipment defect d...Equipment defect detection is essential to the security and stabil-ity of power grid networking operations.Besides the status of the power grid itself,environmental information is also necessary for equipment defect detection.At the same time,different types of intelligent sensors can mon-itor environmental information,such as temperature,humidity,dust,etc.Therefore,we apply the Internet of Things(IoT)technology to monitor the related environment and pervasive interconnections to diverse physical objects.However,the data related to device defects in the existing Internet of Things are complex and lack uniform association hence building a knowledge graph is proposed to solve the problems.Intelligent equipment defect domain ontology is the semantic basis for constructing a defect knowledge graph,which can be used to organize,share,and analyze equipment defect-related knowledge.At present,there are a lot of relevant data in the field of intelligent equipment defects.These equipment defect data often focus on a single aspect of the defect field.It is difficult to integrate the database with various types of equipment defect information.This paper combines the characteristics of existing data sources to build a general intelligent equipment defect domain ontology.Based on ontology,this paper proposed the BERT-BiLSTM-Att-CRF model to recognize the entities.This method solves the problem of diverse entity names and insufficient feature information extraction in the field of equipment defect field.The final experiment proves that this model is superior to other models in precision,recall,and F1 value.This research can break the barrier of multi-source heterogeneous knowledge,build an efficient storage engine for multimodal data,and empower the safety of Industrial applications,data,and platforms in multi-clouds for Internet of Things.展开更多
Knowledge graph technology has distinct advantages in terms of fault diagnosis.In this study,the control rod drive mechanism(CRDM)of the liquid fuel thorium molten salt reactor(TMSR-LF1)was taken as the research objec...Knowledge graph technology has distinct advantages in terms of fault diagnosis.In this study,the control rod drive mechanism(CRDM)of the liquid fuel thorium molten salt reactor(TMSR-LF1)was taken as the research object,and a fault diagnosis system was proposed based on knowledge graph.The subject–relation–object triples are defined based on CRDM unstructured data,including design specification,operation and maintenance manual,alarm list,and other forms of expert experience.In this study,we constructed a fault event ontology model to label the entity and relationship involved in the corpus of CRDM fault events.A three-layer robustly optimized bidirectional encoder representation from transformers(RBT3)pre-training approach combined with a text convolutional neural network(TextCNN)was introduced to facilitate the application of the constructed CRDM fault diagnosis graph database for fault query.The RBT3-TextCNN model along with the Jieba tool is proposed for extracting entities and recognizing the fault query intent simultaneously.Experiments on the dataset collected from TMSR-LF1 CRDM fault diagnosis unstructured data demonstrate that this model has the potential to improve the effect of intent recognition and entity extraction.Additionally,a fault alarm monitoring module was developed based on WebSocket protocol to deliver detailed information about the appeared fault to the operator automatically.Furthermore,the Bayesian inference method combined with the variable elimination algorithm was proposed to enable the development of a relatively intelligent and reliable fault diagnosis system.Finally,a CRDM fault diagnosis Web interface integrated with graph data visualization was constructed,making the CRDM fault diagnosis process intuitive and effective.展开更多
With the construction of new power systems,the power grid has become extremely large,with an increasing proportion of new energy and AC/DC hybrid connections.The dynamic characteristics and fault patterns of the power...With the construction of new power systems,the power grid has become extremely large,with an increasing proportion of new energy and AC/DC hybrid connections.The dynamic characteristics and fault patterns of the power grid are complex;additionally,power grid control is difficult,operation risks are high,and the task of fault handling is arduous.Traditional power-grid fault handling relies primarily on human experience.The difference in and lack of knowledge reserve of control personnel restrict the accuracy and timeliness of fault handling.Therefore,this mode of operation is no longer suitable for the requirements of new systems.Based on the multi-source heterogeneous data of power grid dispatch,this paper proposes a joint entity–relationship extraction method for power-grid dispatch fault processing based on a pre-trained model,constructs a knowledge graph of power-grid dispatch fault processing and designs,and develops a fault-processing auxiliary decision-making system based on the knowledge graph.It was applied to study a provincial dispatch control center,and it effectively improved the accident processing ability and intelligent level of accident management and control of the power grid.展开更多
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...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.展开更多
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 ...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.展开更多
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 valua...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.展开更多
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 prevent...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.展开更多
The evolution of the probability density function of a stochastic dynamical system over time can be described by a Fokker–Planck–Kolmogorov(FPK) equation, the solution of which determines the distribution of macrosc...The evolution of the probability density function of a stochastic dynamical system over time can be described by a Fokker–Planck–Kolmogorov(FPK) equation, the solution of which determines the distribution of macroscopic variables in the stochastic dynamic system. Traditional methods for solving these equations often struggle with computational efficiency and scalability, particularly in high-dimensional contexts. To address these challenges, this paper proposes a novel deep learning method based on prior knowledge with dual training to solve the stationary FPK equations. Initially, the neural network is pre-trained through the prior knowledge obtained by Monte Carlo simulation(MCS). Subsequently, the second training phase incorporates the FPK differential operator into the loss function, while a supervisory term consisting of local maximum points is specifically included to mitigate the generation of zero solutions. This dual-training strategy not only expedites convergence but also enhances computational efficiency, making the method well-suited for high-dimensional systems. Numerical examples, including two different two-dimensional(2D), six-dimensional(6D), and eight-dimensional(8D) systems, are conducted to assess the efficacy of the proposed method. The results demonstrate robust performance in terms of both computational speed and accuracy for solving FPK equations in the first three systems. While the method is also applicable to high-dimensional systems, such as 8D, it should be noted that computational efficiency may be marginally compromised due to data volume constraints.展开更多
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 knowled...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.展开更多
Background: Diabetic eye disease is known as a group of eye problems that diabetic patients may develop as a complication of diabetes and can lead to blindness. They may include Diabetic retinopathy (DR), Cataracts, a...Background: Diabetic eye disease is known as a group of eye problems that diabetic patients may develop as a complication of diabetes and can lead to blindness. They may include Diabetic retinopathy (DR), Cataracts, and Glaucoma. Objectives: This study aims to assess the knowledge, attitude, and practices (KAP) around diabetic eye disease in the general population including patients with DM and non-diabetic people in Medina City, Saudi Arabia. Methods: This is a cross-sectional study involving 385 participants via a self-administered online Questionnaire started in January 2023 in Medina, Saudi Arabia. Results: In total, 339 participants with ages ranged from 18 to more than 60 years with a mean age of 26.8 ± 12.6 years old completed the questionnaire. The majority were females (74.6%), singles (67.8%), and had a university level of education (54.6%). Most of the study participants were found to have poor knowledge levels (67%) in comparison to 33% who had an overall good knowledge of diabetic eye diseases. Knowledge level was found to be higher among old-aged participants and those with a family history of DM (P = 0.001, P = 0.049) respectively. Regarding participants’ attitudes and practices, the study showed good attitudes toward eye care practice for diabetics with half of the participants (50%) reporting self-awareness as a reason that made them undergo the first eye screening. Conclusion: Participants in the present study have poor knowledge and awareness level of diabetic eye disease. Furthermore, positive attitudes and perceptions have been revealed by the participants toward the practice of providing eye care for diabetics. .展开更多
Acknowledgement to reviewers for Vol.35 No.1 issue The editors of this special volume,along with the Editorial Board and Editorial Office of Advance in Polar Science,wish to express deep gratitude for the time investe...Acknowledgement to reviewers for Vol.35 No.1 issue The editors of this special volume,along with the Editorial Board and Editorial Office of Advance in Polar Science,wish to express deep gratitude for the time invested and the meticulous revisions carried out by Enrique Bostelmann(Chile),Michael Burns(USA),Rodolfo Coria(Argentina),Jun Ebersole(USA),Jürgen Kriwet(Austria),Guillermo M.López(Argentina),James Parham(USA),Juan Saad(Argentina),Leonardo Salgado(Argentina),Sergio Soto Acuña(Chile),Washington Jones(Uruguay)and seven anonymous reviewers from Argentina,China,Germany,Poland,and USA.展开更多
Automatic control technology is the basis of road robot improvement,according to the characteristics of construction equipment and functions,the research will be input type perception from positioning acquisition,real...Automatic control technology is the basis of road robot improvement,according to the characteristics of construction equipment and functions,the research will be input type perception from positioning acquisition,real-world monitoring,the process will use RTK-GNSS positional perception technology,by projecting the left side of the earth from Gauss-Krueger projection method,and then carry out the Cartesian conversion based on the characteristics of drawing;steering control system is the core of the electric drive unmanned module,on the basis of the analysis of the composition of the steering system of unmanned engineering vehicles,the steering system key components such as direction,torque sensor,drive motor and other models are established,the joint simulation model of unmanned engineering vehicles is established,the steering controller is designed using the PID method,the simulation results show that the control method can meet the construction path demand for automatic steering.The path planning will first formulate the construction area with preset values and realize the steering angle correction during driving by PID algorithm,and never realize the construction-based path planning,and the results show that the method can control the straight path within the error of 10 cm and the curve error within 20 cm.With the collaboration of various modules,the automatic construction simulation results of this robot show that the design path and control method is effective.展开更多
Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order.Amidst the challenges posed by in...Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order.Amidst the challenges posed by intricate and unpredictable risk factors,knowledge graph technology is effectively driving risk management,leveraging its ability to associate and infer knowledge from diverse sources.This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios.Firstly,employing bibliometric methods,the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge graphs.In the succeeding section,systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge graphs.Objectively comparing and summarizing the strengths and weaknesses of each method,we provide recommendations for addressing the existing challenges in the construction process.Subsequently,categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards,and furnishing a detailed exposition on the applicability of technical routes and methods.Finally,the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed,and relevant improvement suggestions were proposed.Practitioners and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.展开更多
Background and Objectives: Chronic kidney disease (CKD) is now a global public health problem. In low- and middle-income countries such as the Congo, access to dialysis is low and inequitable. The prevention of CKD in...Background and Objectives: Chronic kidney disease (CKD) is now a global public health problem. In low- and middle-income countries such as the Congo, access to dialysis is low and inequitable. The prevention of CKD involves raising awareness among patients at risk, such as those suffering from arterial hypertension (AH), by improving their knowledge of CKD. The objectives of our work were to determine the level of knowledge about CKD among hypertensive patients and to identify the factors associated with a low level of knowledge. Methodology: We conducted a 3-month descriptive and analytical cross-sectional study from 1 August to 30 October 2023 in 3 large public hospitals in Brazzaville (capital of the Republic of Congo). We included: hypertensive patients aged 18 and over who had freely consented to participate in our study and were able to answer the questions on the survey form. Patients with known hypertension who had been followed for less than 3 years and those with known chronic renal failure were not included. Results: The mean age was 58.4 ± 14.4 years (29 - 88 years). There were 121 men and 150 women (sex ratio = 0.8). All the patients were educated;37.2% with a higher level of education and 13.6% with primary education. 24 patients (9%) had a good level of knowledge about CKD and 153 (56%) had poor knowledge. A good level of knowledge was associated with the duration of hypertension, intellectual level and the existence of associated heart disease. Conclusion: Our study reveals a significant lack of knowledge about chronic kidney disease among hypertensive patients in Brazzaville.展开更多
Objective:To elucidate the relationship among knowledge,attitudes,and practices regarding Covid-19 and their relationship with booster vaccination status among women with infertility.Methods:This questionnaire-based c...Objective:To elucidate the relationship among knowledge,attitudes,and practices regarding Covid-19 and their relationship with booster vaccination status among women with infertility.Methods:This questionnaire-based cross-sectional study was performed online and offline among women with infertility who visited an infertility clinic in Jakarta,Indonesia.We assessed the patient’s knowledge,attitudes,and practices regarding Covid-19 and their relationship with booster vaccination status and sociodemographic profile.Results:A total of 178 subjects participated in this study,and most participants(92.6%)had received booster Covid-19 vaccines.From the questionnaire,74.2%had good knowledge,and 99.4%had good attitudes regarding Covid-19;however,only 57.9%of patients had good practices.A weak positive correlation existed between knowledge and attitudes(r=0.11,P=0.13)and a moderate negative correlation between attitudes and practices(r=-0.44,P=0.56).Participants’knowledge about vaccines and infertility was correlated with booster vaccination status(P=0.04).Academic background(P=0.01)and attitudes(P=0.01)were also correlated with booster vaccination status.The significant determinants of hesitance of receiving Covid-19 booster vaccines were high school education or below(OR=0.08,95%CI 0.02-0.36)and poor practices(OR=0.21,95%CI 0.05-0.95).Conclusions:The majority of the participants had received the Covid-19 booster vaccine and had good knowledge and attitudes but poor practices regarding Covid-19.Most participants had poor knowledge about the relationship between infertility and the Covid-19 vaccine.The general population should be more informed and reminded about practices to prevent Covid-19 and the relationship between vaccination and fertility to increase the number of people who receive Covid-19 booster vaccines.展开更多
基金supported in part by the Beijing Natural Science Foundation under Grants L211020 and M21032in part by the National Natural Science Foundation of China under Grants U1836106 and 62271045in part by the Scientific and Technological Innovation Foundation of Foshan under Grants BK21BF001 and BK20BF010。
文摘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.
基金supported by the Natural Science Foundation of China(Grants No.71673136).
文摘As a cultural concept refl ecting the relationship between humans and forests,forest culture plays an active role in sustainable forest management.Forest parks provide a wide range of ecosystem services essential for the sustainable development of society,and the relationships between forest culture,green construction and management of forest parks have practical signifi cance.This study aimed to understand the interaction and process of forest culture infl uencing green construction and management in forest parks with the models Knowledge-Attitude-Practice(KAP)and Theory of Planned Behavior(TPB)by proposing a theoretical model.Four hypotheses were tested using data collected from 193 forest park employees in Heilongjiang Province,China.Our results show that forest culture had a signifi cant infl uence on green construction and forest management.In addition,subjective norm and perceived behavioral control directly impacted behavior in green construction and management of the forest park,whereas attitude did not have an impact.Subjective norm had a direct eff ect on attitude.Results between constructs show that forest culture had an indirect eff ect on planning and construction,and on ecological and economic management.Consequently,it supported three of four hypotheses within the proposed model in determining the infl uence of forest culture on green construction and management.
基金supported by the research project of the Jiangsu water conservancy science and technology project (Contract Number:2021067).
文摘Promoting the co-constructing and sharing of organizational knowledge and improving organizational performance have always been the core research subject of knowledge management.Existing research focuses on the construction of knowledge management systems and knowledge sharing and transfer mechanisms.With the rapid development and application of cloud computing and big data technology,knowledge management is faced with many problems,such as how to combine with the new generation of information technology,how to achieve integration with organizational business processes,and so on.To solve such problems,this paper proposes a reciprocal collaborative knowledge management model(RCKMmodel)based on cloud computing technology,reciprocity theory,and collaboration technology.RCKM model includes project group management and cloud computing technology,which can realize management,finance,communication,and quality assurance of multiple projects and solve the problem of business integration with knowledge management.This paper designs evaluation methods of tacit knowledge and reciprocity preference based on the Bayesian formula and analyzes their effect with simulation data.The methods can provide quantitative support for the integration of knowledge management and business management to realize reciprocity and collaboration in the RCKM model.The research found that RCKM model can fully use cloud computing technology to promote the integration of knowledge management and organizational business,and the evaluation method based on the Bayesian formula can provide relatively accurate data support for the evaluation and selection of project team members.
基金supported in part by the National Natural Science Foundation of China(62302161,62303361)the Postdoctoral Innovative Talent Support Program of China(BX20230114)。
文摘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.
基金supported by the fund project:Research on Basic Capability ofMultimodal Cognitive Graph(Granted No.524608210192).
文摘Equipment defect detection is essential to the security and stabil-ity of power grid networking operations.Besides the status of the power grid itself,environmental information is also necessary for equipment defect detection.At the same time,different types of intelligent sensors can mon-itor environmental information,such as temperature,humidity,dust,etc.Therefore,we apply the Internet of Things(IoT)technology to monitor the related environment and pervasive interconnections to diverse physical objects.However,the data related to device defects in the existing Internet of Things are complex and lack uniform association hence building a knowledge graph is proposed to solve the problems.Intelligent equipment defect domain ontology is the semantic basis for constructing a defect knowledge graph,which can be used to organize,share,and analyze equipment defect-related knowledge.At present,there are a lot of relevant data in the field of intelligent equipment defects.These equipment defect data often focus on a single aspect of the defect field.It is difficult to integrate the database with various types of equipment defect information.This paper combines the characteristics of existing data sources to build a general intelligent equipment defect domain ontology.Based on ontology,this paper proposed the BERT-BiLSTM-Att-CRF model to recognize the entities.This method solves the problem of diverse entity names and insufficient feature information extraction in the field of equipment defect field.The final experiment proves that this model is superior to other models in precision,recall,and F1 value.This research can break the barrier of multi-source heterogeneous knowledge,build an efficient storage engine for multimodal data,and empower the safety of Industrial applications,data,and platforms in multi-clouds for Internet of Things.
基金the Young Potential Program of Shanghai Institute of Applied Physics,Chinese Academy of Sciences(No.E0553101).
文摘Knowledge graph technology has distinct advantages in terms of fault diagnosis.In this study,the control rod drive mechanism(CRDM)of the liquid fuel thorium molten salt reactor(TMSR-LF1)was taken as the research object,and a fault diagnosis system was proposed based on knowledge graph.The subject–relation–object triples are defined based on CRDM unstructured data,including design specification,operation and maintenance manual,alarm list,and other forms of expert experience.In this study,we constructed a fault event ontology model to label the entity and relationship involved in the corpus of CRDM fault events.A three-layer robustly optimized bidirectional encoder representation from transformers(RBT3)pre-training approach combined with a text convolutional neural network(TextCNN)was introduced to facilitate the application of the constructed CRDM fault diagnosis graph database for fault query.The RBT3-TextCNN model along with the Jieba tool is proposed for extracting entities and recognizing the fault query intent simultaneously.Experiments on the dataset collected from TMSR-LF1 CRDM fault diagnosis unstructured data demonstrate that this model has the potential to improve the effect of intent recognition and entity extraction.Additionally,a fault alarm monitoring module was developed based on WebSocket protocol to deliver detailed information about the appeared fault to the operator automatically.Furthermore,the Bayesian inference method combined with the variable elimination algorithm was proposed to enable the development of a relatively intelligent and reliable fault diagnosis system.Finally,a CRDM fault diagnosis Web interface integrated with graph data visualization was constructed,making the CRDM fault diagnosis process intuitive and effective.
基金supported by the Science and Technology Project of the State Grid Corporation“Research on Key Technologies of Power Artificial Intelligence Open Platform”(5700-202155260A-0-0-00).
文摘With the construction of new power systems,the power grid has become extremely large,with an increasing proportion of new energy and AC/DC hybrid connections.The dynamic characteristics and fault patterns of the power grid are complex;additionally,power grid control is difficult,operation risks are high,and the task of fault handling is arduous.Traditional power-grid fault handling relies primarily on human experience.The difference in and lack of knowledge reserve of control personnel restrict the accuracy and timeliness of fault handling.Therefore,this mode of operation is no longer suitable for the requirements of new systems.Based on the multi-source heterogeneous data of power grid dispatch,this paper proposes a joint entity–relationship extraction method for power-grid dispatch fault processing based on a pre-trained model,constructs a knowledge graph of power-grid dispatch fault processing and designs,and develops a fault-processing auxiliary decision-making system based on the knowledge graph.It was applied to study a provincial dispatch control center,and it effectively improved the accident processing ability and intelligent level of accident management and control of the power grid.
基金supported in part by the National Natural Science Foundation of China under Grant 72264036in part by the West Light Foundation of The Chinese Academy of Sciences under Grant 2020-XBQNXZ-020+1 种基金Social Science Foundation of Xinjiang under Grant 2023BGL077the Research Program for High-level Talent Program of Xinjiang University of Finance and Economics 2022XGC041,2022XGC042.
文摘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.
基金This research is supported by the Chinese Special Projects of the National Key Research and Development Plan(2019YFB1405702).
文摘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.
文摘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.
文摘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.
基金Project supported by the National Natural Science Foundation of China (Grant No.12172226)。
文摘The evolution of the probability density function of a stochastic dynamical system over time can be described by a Fokker–Planck–Kolmogorov(FPK) equation, the solution of which determines the distribution of macroscopic variables in the stochastic dynamic system. Traditional methods for solving these equations often struggle with computational efficiency and scalability, particularly in high-dimensional contexts. To address these challenges, this paper proposes a novel deep learning method based on prior knowledge with dual training to solve the stationary FPK equations. Initially, the neural network is pre-trained through the prior knowledge obtained by Monte Carlo simulation(MCS). Subsequently, the second training phase incorporates the FPK differential operator into the loss function, while a supervisory term consisting of local maximum points is specifically included to mitigate the generation of zero solutions. This dual-training strategy not only expedites convergence but also enhances computational efficiency, making the method well-suited for high-dimensional systems. Numerical examples, including two different two-dimensional(2D), six-dimensional(6D), and eight-dimensional(8D) systems, are conducted to assess the efficacy of the proposed method. The results demonstrate robust performance in terms of both computational speed and accuracy for solving FPK equations in the first three systems. While the method is also applicable to high-dimensional systems, such as 8D, it should be noted that computational efficiency may be marginally compromised due to data volume constraints.
文摘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.
文摘Background: Diabetic eye disease is known as a group of eye problems that diabetic patients may develop as a complication of diabetes and can lead to blindness. They may include Diabetic retinopathy (DR), Cataracts, and Glaucoma. Objectives: This study aims to assess the knowledge, attitude, and practices (KAP) around diabetic eye disease in the general population including patients with DM and non-diabetic people in Medina City, Saudi Arabia. Methods: This is a cross-sectional study involving 385 participants via a self-administered online Questionnaire started in January 2023 in Medina, Saudi Arabia. Results: In total, 339 participants with ages ranged from 18 to more than 60 years with a mean age of 26.8 ± 12.6 years old completed the questionnaire. The majority were females (74.6%), singles (67.8%), and had a university level of education (54.6%). Most of the study participants were found to have poor knowledge levels (67%) in comparison to 33% who had an overall good knowledge of diabetic eye diseases. Knowledge level was found to be higher among old-aged participants and those with a family history of DM (P = 0.001, P = 0.049) respectively. Regarding participants’ attitudes and practices, the study showed good attitudes toward eye care practice for diabetics with half of the participants (50%) reporting self-awareness as a reason that made them undergo the first eye screening. Conclusion: Participants in the present study have poor knowledge and awareness level of diabetic eye disease. Furthermore, positive attitudes and perceptions have been revealed by the participants toward the practice of providing eye care for diabetics. .
文摘Acknowledgement to reviewers for Vol.35 No.1 issue The editors of this special volume,along with the Editorial Board and Editorial Office of Advance in Polar Science,wish to express deep gratitude for the time invested and the meticulous revisions carried out by Enrique Bostelmann(Chile),Michael Burns(USA),Rodolfo Coria(Argentina),Jun Ebersole(USA),Jürgen Kriwet(Austria),Guillermo M.López(Argentina),James Parham(USA),Juan Saad(Argentina),Leonardo Salgado(Argentina),Sergio Soto Acuña(Chile),Washington Jones(Uruguay)and seven anonymous reviewers from Argentina,China,Germany,Poland,and USA.
文摘Automatic control technology is the basis of road robot improvement,according to the characteristics of construction equipment and functions,the research will be input type perception from positioning acquisition,real-world monitoring,the process will use RTK-GNSS positional perception technology,by projecting the left side of the earth from Gauss-Krueger projection method,and then carry out the Cartesian conversion based on the characteristics of drawing;steering control system is the core of the electric drive unmanned module,on the basis of the analysis of the composition of the steering system of unmanned engineering vehicles,the steering system key components such as direction,torque sensor,drive motor and other models are established,the joint simulation model of unmanned engineering vehicles is established,the steering controller is designed using the PID method,the simulation results show that the control method can meet the construction path demand for automatic steering.The path planning will first formulate the construction area with preset values and realize the steering angle correction during driving by PID algorithm,and never realize the construction-based path planning,and the results show that the method can control the straight path within the error of 10 cm and the curve error within 20 cm.With the collaboration of various modules,the automatic construction simulation results of this robot show that the design path and control method is effective.
基金supported by the Shandong Province Science and Technology Project(2023TSGC0509,2022TSGC2234)Qingdao Science and Technology Plan Project(23-1-5-yqpy-2-qy).
文摘Enterprise risk management holds significant importance in fostering sustainable growth of businesses and in serving as a critical element for regulatory bodies to uphold market order.Amidst the challenges posed by intricate and unpredictable risk factors,knowledge graph technology is effectively driving risk management,leveraging its ability to associate and infer knowledge from diverse sources.This review aims to comprehensively summarize the construction techniques of enterprise risk knowledge graphs and their prominent applications across various business scenarios.Firstly,employing bibliometric methods,the aim is to uncover the developmental trends and current research hotspots within the domain of enterprise risk knowledge graphs.In the succeeding section,systematically delineate the technical methods for knowledge extraction and fusion in the standardized construction process of enterprise risk knowledge graphs.Objectively comparing and summarizing the strengths and weaknesses of each method,we provide recommendations for addressing the existing challenges in the construction process.Subsequently,categorizing the applied research of enterprise risk knowledge graphs based on research hotspots and risk category standards,and furnishing a detailed exposition on the applicability of technical routes and methods.Finally,the future research directions that still need to be explored in enterprise risk knowledge graphs were discussed,and relevant improvement suggestions were proposed.Practitioners and researchers can gain insights into the construction of technical theories and practical guidance of enterprise risk knowledge graphs based on this foundation.
文摘Background and Objectives: Chronic kidney disease (CKD) is now a global public health problem. In low- and middle-income countries such as the Congo, access to dialysis is low and inequitable. The prevention of CKD involves raising awareness among patients at risk, such as those suffering from arterial hypertension (AH), by improving their knowledge of CKD. The objectives of our work were to determine the level of knowledge about CKD among hypertensive patients and to identify the factors associated with a low level of knowledge. Methodology: We conducted a 3-month descriptive and analytical cross-sectional study from 1 August to 30 October 2023 in 3 large public hospitals in Brazzaville (capital of the Republic of Congo). We included: hypertensive patients aged 18 and over who had freely consented to participate in our study and were able to answer the questions on the survey form. Patients with known hypertension who had been followed for less than 3 years and those with known chronic renal failure were not included. Results: The mean age was 58.4 ± 14.4 years (29 - 88 years). There were 121 men and 150 women (sex ratio = 0.8). All the patients were educated;37.2% with a higher level of education and 13.6% with primary education. 24 patients (9%) had a good level of knowledge about CKD and 153 (56%) had poor knowledge. A good level of knowledge was associated with the duration of hypertension, intellectual level and the existence of associated heart disease. Conclusion: Our study reveals a significant lack of knowledge about chronic kidney disease among hypertensive patients in Brazzaville.
文摘Objective:To elucidate the relationship among knowledge,attitudes,and practices regarding Covid-19 and their relationship with booster vaccination status among women with infertility.Methods:This questionnaire-based cross-sectional study was performed online and offline among women with infertility who visited an infertility clinic in Jakarta,Indonesia.We assessed the patient’s knowledge,attitudes,and practices regarding Covid-19 and their relationship with booster vaccination status and sociodemographic profile.Results:A total of 178 subjects participated in this study,and most participants(92.6%)had received booster Covid-19 vaccines.From the questionnaire,74.2%had good knowledge,and 99.4%had good attitudes regarding Covid-19;however,only 57.9%of patients had good practices.A weak positive correlation existed between knowledge and attitudes(r=0.11,P=0.13)and a moderate negative correlation between attitudes and practices(r=-0.44,P=0.56).Participants’knowledge about vaccines and infertility was correlated with booster vaccination status(P=0.04).Academic background(P=0.01)and attitudes(P=0.01)were also correlated with booster vaccination status.The significant determinants of hesitance of receiving Covid-19 booster vaccines were high school education or below(OR=0.08,95%CI 0.02-0.36)and poor practices(OR=0.21,95%CI 0.05-0.95).Conclusions:The majority of the participants had received the Covid-19 booster vaccine and had good knowledge and attitudes but poor practices regarding Covid-19.Most participants had poor knowledge about the relationship between infertility and the Covid-19 vaccine.The general population should be more informed and reminded about practices to prevent Covid-19 and the relationship between vaccination and fertility to increase the number of people who receive Covid-19 booster vaccines.
