A Semantic Network Analysis of News Comments on Child Abuse: Focusing on the Major Changes of Child Abuse Policies

Objectives: This study analyzes how pulic awareness of perception of child abuse and the recent child abuse policy changes appeared in the news comments about child abuse. The major policy changes include the Act on Special Cases Concerning The Punishment, Etc. of Child Abuse Crimes (Act No. 15255, Dec. 19, 2017), Mandatory CCTV Installation at Daycare Centers (2015), investigation for school children who have been absent school long-term (2016), the 100 state tasks in inclusive welfare (2017), e-Child Happiness Support Service (2018), and Strengthening the Publicness of Child Protection Service (2019).Methods: For the purpose, this study analyzed 1,333,677 comments on news about child abuse from 1 January 2014 to 31 December 2019. In this study, we conducted semantic network analysis to analyze how the contents of child abuse appeared in child abuse comments and the policy contents appeared at the time when major policies were implemented. The analysis using R program.Results: As a result of the analysis, the study found that the public recognized child abuse as a crime. Second, stereotypes on the perpetrators of child abuse were identified. Third, it was confirmed that the public is deeply interested in child abuse incidents occurred at kindergartens and daycare centers. Lastly, the result has revealed that the public, in general, does not yet acknowledge changes on the central policy of child abuse.Conclusion: Based on these findings, policy implcations are discussed to make improvements in awareness of child abuse more accessible to the public. Specifically, The government is responsible for solving stereotypes of child abuse, improving trust in daycare centers, and providing information on child care policies to the public.</jats:p

Fast cycle-accurate behavioral simulation for pipelined processors using early pipeline evaluation

Modeling and simulating pipelined processors in procedural languages such as C/C++ requires lots of cost in handling concurrent events, which hinders fast simulation. A number of researches on simulation have devised speed-up techniques to reduce the number of events. This paper presents a new simulation approach developed to enhance the simulation of pipelined processors. The proposed approach is based on early pipeline evaluation that all the intermediate values of an instruction are computed in advance, creating a future state for the next instructions. The future state allows the next instructions to be computed without considering data dependencies between nearby instructions. We apply this concept to building a cycleaccurate simulator for a pipelined RISC processor and achieve almost the same speed as the instruction-level simulator. 1

Impact of the COVID-19 Pandemic on the Developmental Outcomes among Korean Kinship Foster Care Children: Gender Differences

(1) Background: This study explored changes before and during the COVID-19 pandemic in terms of developmental outcomes among kinship foster care children in the Republic of Korea: and gender differences in the changes; (2) Methods: The study analyzed the data of 217 kinship care children who participated in both the first- and second-wave surveys of the Panel Study of Korean Foster Care Children. As the main statistical methods, we utilized repeated-measures ANOVA and analysis of covariance (ANCOVA); (3) Results: Analysis of developmental outcomes measured before and during the pandemic showed no significant changes. However, significant interaction effects existed between time (before and during the pandemic) and gender, indicating that boys and girls recorded different patterns of change before and during the COVID-19 pandemic; (4) Discussion:During the COVID-19 pandemic, girls experienced negative changes in most areas of development, whereas boys experienced positive changes. The policy and practical implications for foster care children in Korea were discussed

Weld-Quality Prediction Algorithm Based on Multiple Models Using Process Signals in Resistance Spot Welding

An efficient nondestructive testing method of resistance spot weld quality is essential in evaluating the weld quality of all welded joints in the automotive components of a car body production line. This study proposes a quality prediction algorithm for resistance spot welding that can predict the geometrical and physical properties of a spot-welded joint and evaluate weld quality based on quality acceptance criteria. To this end, four statistical models that predict the main geometrical and physical properties of a spot-welded joint, including tensile shear strength, indentation depth, expulsion occurrence, and failure mode, were estimated based on material information, dynamic resistance, and electrode displacement signals. The significance of the estimated models was then verified through an analysis of variance. The prediction accuracies of the models were 94.3%, 93.4%, 97.5%, and 85.0% for the tensile shear strength, indentation depth, expulsion occurrence, and failure modes, respectively. A weld quality evaluation methodology that can predict the properties of a spot-welded joint and evaluate the overall quality requirements based on authorized welding standards was proposed using the four statistical models

Weld-Quality Prediction Algorithm Based on Multiple Models Using Process Signals in Resistance Spot Welding

An efficient nondestructive testing method of resistance spot weld quality is essential in evaluating the weld quality of all welded joints in the automotive components of a car body production line. This study proposes a quality prediction algorithm for resistance spot welding that can predict the geometrical and physical properties of a spot-welded joint and evaluate weld quality based on quality acceptance criteria. To this end, four statistical models that predict the main geometrical and physical properties of a spot-welded joint, including tensile shear strength, indentation depth, expulsion occurrence, and failure mode, were estimated based on material information, dynamic resistance, and electrode displacement signals. The significance of the estimated models was then verified through an analysis of variance. The prediction accuracies of the models were 94.3%, 93.4%, 97.5%, and 85.0% for the tensile shear strength, indentation depth, expulsion occurrence, and failure modes, respectively. A weld quality evaluation methodology that can predict the properties of a spot-welded joint and evaluate the overall quality requirements based on authorized welding standards was proposed using the four statistical models.</jats:p