Analysis of differences in variables related to health and safety according to the employment type of Korean workers

Introduction: The purpose of this study was to understand the differences in variables related to health and safety according to the employment type of Korean workers, specifically to identify the differences by employment type on in health status, the likelihood of wearing protective gear when working, access to manuals on emotional expression, and access to information on risk factors related to health and safety. Methods: The secondary data of four items on employment type, health type of workers and safety among the 5th Korean Working Condition Survey conducted in 2017 in Korea was used in this study. The data of workers were processed by using SPSS/WIN 23.0 Program and R 3.1.2, and demographic characteristics were quantified as frequency and percentage.  Results: A total of 30,300 employed people were surveyed. The result shows that part-time workers have poorer health than full-time workers (c2 = 540.7155, p < 0.05), insufficiently wore protective gear (c2 = 24.8702, p < 0.05), had insufficient access to manuals on emotional expression (c2 = 27.7612, p < 0.05) and lacked information about risk factors (c2 = 185.0082, p < 0.05). Conclusion: Health and safety manager will need to have education and consultation, development of manual and perform an early intervention to improve safety environment as primary health care providers by understanding factors related to health and safety of part-time workers

Analysis of the semantic network of post-traumatic stress disorder using Korean social big data

Introduction: In this study, we wanted to examine how post-traumatic stress disorder was discussed in Korean newspaper articles with semantic network analysis suitable for unstructured big data analysis. Methods: This study analyzed 11,304 articles related to post-traumatic stress reported by four major Korean newspapers for three years from July 30, 2017, to July 30, 2020. R 3.6.2 program was used to calculate TF and TF-IDF values, and UCINET 6.0 and interlocked NetDraw was used for DC, EC, and CONCOR values. Results: As a result of deriving 50 major keywords with high TF-IDF values ​​in newspaper articles related to a post-traumatic stress disorder, TF-IDF values were high in the order of 'sick leave', 'solitary confinement', 'detention center', 'standing order', and 'prisoner'. As a result of conducting a CONCOR analysis to determine which sub-clusters keywords are classified into, the researcher derived each cluster based on words included: 'PTSD by crops' (cluster 1), 'PTSD by broadcasting accidents' (clusters), 'PTSD by farm livestock accidents' (cluster 3), and 'PTSD by various accidents' (cluster 4). Conclusions Based on the research results, post-traumatic stress disorder needs to be managed nationally. As such, we intend to provide basic data for policy development and intervention programs

Mobility-Induced Graph Learning for WiFi Positioning

A smartphone-based user mobility tracking could be effective in finding his/her location, while the unpredictable error therein due to low specification of built-in inertial measurement units (IMUs) rejects its standalone usage but demands the integration to another positioning technique like WiFi positioning. This paper aims to propose a novel integration technique using a graph neural network called Mobility-INduced Graph LEarning (MINGLE), which is designed based on two types of graphs made by capturing different user mobility features. Specifically, considering sequential measurement points (MPs) as nodes, a user's regular mobility pattern allows us to connect neighbor MPs as edges, called time-driven mobility graph (TMG). Second, a user's relatively straight transition at a constant pace when moving from one position to another can be captured by connecting the nodes on each path, called a direction-driven mobility graph (DMG). Then, we can design graph convolution network (GCN)-based cross-graph learning, where two different GCN models for TMG and DMG are jointly trained by feeding different input features created by WiFi RTTs yet sharing their weights. Besides, the loss function includes a mobility regularization term such that the differences between adjacent location estimates should be less variant due to the user's stable moving pace. Noting that the regularization term does not require ground-truth location, MINGLE can be designed under semi- and self-supervised learning frameworks. The proposed MINGLE's effectiveness is extensively verified through field experiments, showing a better positioning accuracy than benchmarks, say root mean square errors (RMSEs) being 1.398 (m) and 1.073 (m) for self- and semi-supervised learning cases, respectively.Comment: submitted to a possible IEEE journa