A Study on the Educational Significance Guidance Plan Value of the Jumong Myth Using STEAM

Jumong is a legendary figure in Korean mythology.He is depicted as a genius archer,a brilliant mind with horses,a god who rules the rivers and the sky.Jumong is a mythical character who is believed to be of divine descent and to possess superhuman abilities that humans cannot match.The myth of Jumong is a very competitive content that can compete on a global scale.However,in order for it to be successful,it is necessary to be able to reinterpret our mythology to suit the times and reproduce it culturally.To this end,the realistic globalization of Korean classical literature should commence with the genre of Korean mythology.This paper presents the educational significance of the Jumong myth as a teaching-learning model,via STEAM(science,technology,engineering,arts,and mathematics),on the theme of Korean mythology.As we enter the era of artificial intelligence(AI)through the 4th Industrial Revolution,the most appropriate teaching and learning method,the convergence class,will provide an opportunity for students living in modern times to discover the cultural archetypes that allow them to recognise themselves as individuals and us as a collective,and to find the roots of the myth to positively renew their identity.Furthermore,it is my hope that they will rediscover and appreciate the representative work of Korean mythology,Gojomong,the eponymous story of Goguryeo.

The application of omics technologies to toxicology

The application of omics technologies,including genomics,transcriptomics,proteomics and metabolomics,has the potential to revolutionize toxicology by providing a more comprehensive understanding of the molecular mechanisms of toxicity,identifying potential biomarkers of exposure or effect,and enabling personalized risk assessments for individuals.Each omics approach has its own challenges,including data analysis and interpretation,but the integration of multiple omics approaches can provide a more comprehensive understanding of toxicity.The use of omics technologies for personalized risk assessments can inform targeted interventions and improve public health outcomes.While challenges remain,the potential benefits of omics technologies in toxicology make it an exciting area of research for the future.

Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

Recent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks(CNNs).However,these methods focus primarily on predicting generally perceived preference of an image,making them usually have limited practicability,since each user may have completely different preferences for the same image.To address this problem,this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste.We achieve this in a coarse to fine manner,by joint regression and learning from pairwise rankings.Specifically,we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs.We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores,and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression.Next,we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss.Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences,clearly outperforming state-of-the-art methods.Moreover,we show that the learned personalized image aesthetic benefits a wide variety of applications.