An evaluation of critical knowledge areas for managing the COVID-19 pandemic

Purpose The ability to manage the COVID-19 pandemic is contingent upon the ability to effectively manage its heterogeneous knowledge resources. Knowledge mapping represents a great opportunity to create value by bringing stakeholders together, facilitating comprehensive collaboration and facilitating broader in-depth knowledge sharing and transfer. However, identifying and analysing critical knowledge areas is one of the most important steps when creating a knowledge map. Therefore, the purpose of this paper is to appraise the critical knowledge areas for managing COVID-19, and thereby enhance decision-making in tackling the consequences of the pandemic. Design/methodology/approach The methodological approach for this study is a critical literature review, covering publications on knowledge management, knowledge mapping and COVID-19. EBSCOhost, PubMed, Scopus, Science Direct, TRID, Web of Science and Wiley Online Library were searched for full text, peer-reviewed articles written in English that investigated on critical knowledge areas for managing the spread of COVID-19. After full screening, 21 articles met the criteria for inclusion and were analysed and reported. Findings The study revealed seven critical knowledge areas for managing the COVID-19 pandemic. These are cleaning and disinfection; training, education and communication; reporting guidance and updates; testing; infection control measures, personal protective equipment; and potential COVID-19 transmission in health and other care settings. The study developed a concept knowledge map illustrating areas of critical knowledge which decision-makers need to be aware of. Practical implications Providing decision-makers with access to key knowledge during the COVID-19 pandemic seems to be crucial for effective decision-making. This study has provided insights for the professionals and decision-makers identifying the critical knowledge areas for managing the COVID-19 pandemic. Social implications The study advances the literature on knowledge management and builds a theoretical link with the management of public health emergencies. Additionally, the findings support the theoretical position that knowledge maps facilitate decision-making and help users to identify critical knowledge areas easily and effectively. Originality/value This study fills gaps in the existing literature by providing an explicit representation of know-how for managing the COVID-19 pandemic. This paper uses an objective and qualitative approach by reviewing related publications, reports and guidelines in the analysis. The concept map illustrates the critical knowledge areas for managing the COVID-19 pandemic.

Construction and Application of Korean-English-Japanese Multilingual Teaching Aid System Based on Knowledge Map

This article describes the design and application of a computer-based system for simultaneously teaching Korean, English and Japanese languages in a classroom setting using knowledge visualization techniques to show the relationships between vocabularies, grammars and meanings. The system consists of a knowledge database of Korean, English, and Japanese which is then uploaded into the teaching module. Visualizations of this information in the form of knowledge maps based upon generally accepted rules of knowledge map can then be displayed and contrasted using the system interface to enter user queries. The system is then tested in a blended classroom of native Korean speakers. Data on student learning experiences are then gathered by means of a questionnaire and analyzed in order to assess the overall success of knowledge acquisition in this setting. Our findings show that this system evokes a personal initiative in the learning process, facilitates communication between teachers and learners, and supports the rapid acquisition of multilingual knowledge.

Research prospect of autonomous driving decision technology under complex traffic scenarios

Decision-making system is the essential part of the autonomous vehicle “brain”, which determines the safety and stability of vehicles, and is also the key to reflect the intelligent level of autonomous vehicles. Compared with simple scenarios such as expressway, urban traffic scenarios have the characteristics of complex and frequent interaction between traffic participants. Carrying out in-depth research on complex traffic scenarios and optimizing autonomous decision-making algorithms are the key methods for the purpose of promoting the application of autonomous driving technologies. In the future, we can further combine the artificial intelligence methods such as cognitive or knowledge map, behaviour prediction of traffic participants, and humanoid intelligence, so as to enhance the intelligent level of autonomous driving.

Integrating Domain Knowledge into Deep Learning for Skin Lesion Risk Prioritization to Assist Teledermatology Referral

Teledermatology has developed rapidly in recent years and is nowadays an essential tool for early diagnosis. In this work, we aim to improve existing Teledermatology processes for skin lesion diagnosis by developing a deep learning approach for risk prioritization with a dataset of retrospective data from referral requests of the Portuguese National Health System. Given the high complexity of this task, we propose a new prioritization pipeline guided and inspired by domain knowledge. We explored automatic lesion segmentation and tested different learning schemes, namely hierarchical classification and curriculum learning approaches, optionally including additional patient metadata. The final priority level prediction can then be obtained by combining predicted diagnosis and a baseline priority level accounting for explicit expert knowledge. In both the differential diagnosis and prioritization branches, lesion segmentation with 30% tolerance for contextual information was shown to improve classification when compared with a flat baseline model trained on original images; furthermore, the addition of patient information was not beneficial for most experiments. Curriculum learning delivered better results than a flat or hierarchical approach. The combination of diagnosis information and a knowledge map, created in collaboration with dermatologists, together with the priority achieved interesting results (best macro F1 of 43.93% for a validated test set), paving the way for new data-centric and knowledge-driven approaches.

Model Research on In-Service Learning of Intellectualization of Aerospace Knowledge

With the development of vocational education, it is necessary to construct the pattern of lifelong learning. To push delivery learning resources and provide a learning environment, it is necessary to innovate in-service learning mode. According to the characteristics of the aerospace position, the capacity model was studied and proposed. Based on the ability model, the intelligent in-service learning model is studied and proposed to improve the precision service quality. From the angle of principle and learning process, this paper discusses the intelligent in-service learning mode of including the learning model based on knowledge map and the learning model based on seminar hall. The framework of the job knowledge map is constructed according to the post ability model which is based on professional knowledge, professional skills, and professional quality. The intelligent on-the-job learning model includes four elements: (i) learning platform, (ii) learning resources, (iii) learning methods, and (iv) learning evaluation. The learning portrait can record and visualize the information of learning, including content, activities, and effects.

Mind Map Construction for English Grammar Teaching Based on Knowledge Map

The analysis of the frontier issues of the English language teaching method in China is of great guidance for English language teaching. Based on the ontology model of English teaching domain, the knowledge map of English teaching in colleges and universities is constructed by fusing heterogeneous English subject data from multiple sources. Firstly, we obtain domain knowledge from relevant websites and existing documents through web crawlers and other techniques and clean the data based on BERT model; then, we use Word2Vec to judge the similarity between the research directions of characters and solve the entity alignment problem; based on the scientific knowledge map theory, we count the frequency of keywords in each year and analyze them to describe the association and union between keywords. It can explain the current situation and trend, rise and fall, disciplinary growth points, and breakthroughs of ELT. Through keyword analysis, the hot issues mainly revolve around ELT, English teaching, college English, grammar-translation method, curriculum reform, and so forth, to realize the quick query and resource statistics of ELT basic data, in order to promote the subsequent English discipline assessment work to be completed more efficiently.

A Bibliometric and Visualization Analysis of Mindfulness and Meditation Research from 1900 to 2021

This study comprehensively summarizes research in the field of meditation, especially mindfulness meditation from 1900 to 2021, by analyzing the knowledge map through CiteSpace and VOSviewer software. Using “mindfulness *” or “meditation *” as the topic, articles included in the Science Citation Index Expanded and Social Sciences Citation Index were searched in the web of science core database, resulting in the selection of 19,752 articles. Over half a century ago, Deikman published the field’s first article in the Journal of Nervous and Mental Disease in 1963, and publications have soared in subsequent decades. The USA is in the core position in terms of global collaboration, total publication numbers, and total citations. The Mindfulness journal ranked first for the most published articles and citations. “The benefits of being present: Mindfulness and its role in psychological well-being,” written by Brown and Ryan, was the most cited article. Mindfulness, meditation, depression, intervention, stress reduction, stress, and anxiety are the top co-occurrence keywords. The timeline of cluster analysis discloses that before 2010, hypertension, cancer, mindfulness, generalized anxiety disorder, and other topics received great attention. In the decade since 2010, scholars have shown interest in meta-analysis, attention, and self-assessment, and keen attention to mindfulness-based interventions. These findings provide an important foundation to direct future research.