The Potential Benefits of Japanese MMORPGs for Japanese Learning Motivation

Foreign language anxiety (FLA) has been found to have a negative impact on the motivation to learn foreign languages in many previous research studies. However, recent studies have found that massively multiplayer online role-playing games (MMORPGs) in particular provided an environment that positively impacted English as a second language (ESL) acquisition. However, there is a lack of study on the Japanese language and Japanese MMORPGs. Therefore, the current study aims to look at Japanese FLA and integrative motivation in a Japanese MMORPG learning environment as compared to a Japanese classroom learning environment with a sample of 132 English native speakers interested in Japanese in the US. It is predicted that lower Japanese language anxiety (JLA) and higher integrative motivation will be experienced while learning Japanese in MMORPGs than in a classroom setting. A mediation of JLA on the correlation between time spent in MMORPGs and integrative motion was also predicted. The study suggests an alternative way of learning Japanese

Web Technologies (GGC)

This Grants Collection for Web Technologies was created under a Round Nine ALG Textbook Transformation Grant. Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process. Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials: Linked Syllabus Initial Proposal Final Reporthttps://oer.galileo.usg.edu/compsci-collections/1015/thumbnail.jp

Study and Design of an Intelligent Preconditioner Recommendation System

There are many scientific applications in which there is a need to solve very large linear systems. The preconditioned Krylove subspace methods are considered the preferred methods in this field. The preconditioners employed in the preconditioned iterative solvers usually determine the overall convergence rate. However, choosing a good preconditioner for a specific sparse linear system arising from a particular application is the combination of art and science, and presents a formidable challenge for many design engineers and application scientists who do not have much knowledge of preconditioned iterative methods. We tackled the problem of choosing suitable preconditioners for particular applications from a nontraditional point of view. We used the techniques and ideas in knowledge discovery and data mining to extract useful information and special features from unstructured sparse matrices and analyze the relationship between these features and the solving status of the spearse linear systems generated from these sparse matrices. We have designed an Intelligent Preconditioner Recommendation System, which can provide advice on choosing a high performance preconditioner as well as suitable parameters for a given sparse linear system. This work opened a new research direction for a very important topic in large scale high performance scientific computing. The performance of the various data mining algorithms applied in the recommendation system is directly related to the set of matrix features used in the system. We have extracted more than 60 features to represent a sparse matrix. We have proposed to use data mining techniques to predict some expensive matrix features like the condition number. We have also proposed to use the combination of the clustering and classification methods to predict the solving status of a sparse linear system. For the preconditioners with multiple parameters, we may predict the possible combinations of the values of the parameters with which a given sparse linear system may be successfully solved. Furthermore, we have proposed an algorithm to find out which preconditioners work best for a certain sparse linear system with what parameters

Enhancing clustering blog documents by utilizing author/reader comments

Blogs are a new form of internet phenomenon and a vast ever-increasing information resource. Mining blog files for information is a very new research direction in data mining. Blog files are different from standard web files and may need specialized mining strategies. We propose to include the title, body, and comments of the blog pages in clustering datasets from blog documents. In particular, we argue that the author/reader comments of the blog pages may have more discriminating effect in clustering blog documents. We constructed a word-page matrix by downloading blog pages from a well-known website and experimented a k-means clustering algorithm with different weights assigned to the title, body, and comment parts. Our experimental results show that assigning a larger weight value to the blog comments helps the k-means algorithm produce better clustering solutions. The experimental results confirm our hypothesis that the author/reader comments of the blog files are very useful in discriminating blog files

A review of automated sleep disorder detection

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand

Assessment of disaster preparedness and related impact factors among emergency nurses in tertiary hospitals: descriptive cross-sectional study from Henan Province of China

BackgroundThe aim of this study was to investigate the current state of disaster preparedness and to determine associated factors among emergency nurses from tertiary hospitals in Henan Province of China.MethodsThis multicenter descriptive cross-sectional study was conducted with emergency nurses from 48 tertiary hospitals in Henan Province of China between September 7, 2022–September 27, 2022. Data were collected through a self-designeds online questionnaire using the mainland China version of the Disaster Preparedness Evaluation Tool (DPET-MC). Descriptive analysis and multiple linear regression analysis were used to evaluate disaster preparedness and to determine factors affecting disaster preparedness, respectively.ResultsA total of 265 emergency nurses in this study displayed a moderate level of disaster preparedness with a mean item score of 4.24 out 6.0 on the DPET-MC questionnaire. Among the five dimensions of the DPET-MC, the mean item score for pre-disaster awareness was highest (5.17 ± 0.77), while that for disaster management (3.68 ± 1.36) was the lowest. Female gender (B = −9.638, p = 0.046) and married status (B = −8.618, p = 0.038) were negatively correlated with the levels of disaster preparedness. Five factors positively correlated with the levels of disaster preparedness included having attended in the theoretical knowledge training of disaster nursing since work (B = 8.937, p = 0.043), having experienced the disaster response (B = 8.280, p = 0.036), having participated in the disaster rescue simulation exercise (B = 8.929, p = 0.039), having participated in the disaster relief training (B = 11.515, p = 0.025), as well as having participated in the training of disaster nursing specialist nurse (B = 16.101, p = 0.002). The explanatory power of these factors was 26.5%.ConclusionEmergency nurses in Henan Province of China need more education in all areas of disaster preparedness, especially disaster management, which needs to be incorporated into nursing education, including formal and ongoing education. Besides, blended learning approach with simulation-based training and disaster nursing specialist nurse training should be considered as novel ways to improve disaster preparedness for emergency nurses in mainland China