Setting time of construction gypsum, dental plaster, and white orthodontic gypsum.

Background. Dental plaster, white orthodontic gypsum, and construction gypsum have ß-hemihydrate particles. Setting time is an essential property of dental gypsum, which can affect the strength of the material. This research aimed to compare construction gypsum, dental plaster, and white orthodontic gypsum's initial and final setting times. Methods. Three groups were included in this experimental laboratory study: construction gypsum (A), dental plaster (B), and white orthodontic gypsum (C). Each group consisted of 10 samples. Gypsum manipulation consisted of using 120 gr of powder and 60 mL of water. Gypsum powder and water were mixed using a gypsum mixer at 120 rpm. A homogeneous mixture was poured into a mold, and the setting time was measured using a Gillmore needle, according to ASTM C266-03. The initial setting time test was measured using 113.4 grams and a 2.12-mm needle. The final setting time was measured using 453.6 grams and a 1.06-mm needle. This test was repeated until the needle failed to penetrate the gypsum's surface. All the data were analyzed with one-way ANOVA and post hoc Tukey tests using SPSS 23. Results. The average initial setting time for groups A, B, and C were 10.39±1.19, 16.17±1.40, and 24.46±1.51, respectively. The average final setting time for groups A, B, and C were 15.97±0.79, 24.31±0.88) and 33.37±0.66, respectively. One-way ANOVA and post hoc Tukey tests showed significant differences in the initial and final setting times between the three groups (P<0.05). Conclusion. There were differences in setting time between dental plaster, white orthodontic gypsum, and construction gypsum. The construction gypsum's setting time is suitable as a type II dental gypsum, according to ADA No.25.

Use of Artificial Neuron Network to Predict Dental Arch Form

Objective: To develop diagnostic reference for arch form using Artificial Neuron Network (ANN) from tooth size and arch dimension variables on scanned-dental cast from patients with class I malocclusion treated orthodontically. Material and Methods: One hundred and ninety pairs of dental cast pre-post orthodontic treatment gathered from Orthodontic clinics were scanned and then all dimension variables were measured using Image Tool (gender, tooth size and arch dimension). The multivariate data were analyzed statistically using Stata (Lakeway Drive, College Station, Texas USA). The statistic results were compiled to build the neuron network software for analyzing arch form. Results: Gender and all variables from pre-treatment do not influence arch form. Intercanine width, canine depth, intermolar width, and molar depth are variables that influence arch form. The result of the statistical analyses can be used to develop software based on artificial neural network. Output program is the arch form, such as oval, square or tapered. The software can describe arch form with the accuracy of 76.31%. Conclusion: A software using Artificial Neuron Network to describe arch form can be used for diagnostic reference to Class I malocclusion.

Parent educators for teenage smoking behavior.

Background Several studies have shown that there is a significant relationship between teenagers and parental smoking behavior. This study was to empower parent (smokers and non-smokers) to raise parents' concern, to describe teenagers' knowledge about smoking and its dangers, to reduce the status of teenage smoking behavior, and to decrease the number of cigarettes smoked/day. Methods This was a quasi-experimental study, with a pretest-posttest group design. The respondents were 649 students from the 8th grade in junior high schools from three provinces in Indonesia divided into two intervention groups and one control group. The dependent variable was the implementation of parent educators (smokers and non-smokers) who were trained about smoking and its dangers. Data were analyzed by analysis of variance (ANOVA) and the Kruskal-Wallis test, with a significant level of 0.05. Results The results showed that the smoking parent educator model increased the frequency of parent concern significantly and improved knowledge about the dangers of smoking insignificantly; whereas the non-smoker parent educator model decreased the number of respondents' frequent smoking status insignificantly and reduced the number of cigarettes smoked /day by the frequent smoker respondents significantly. Conclusion The parent educator model can be used for preventing teenage smoking behavior.

Jayapura Teenagers Smoking Behavior.

Smoking behavior is a threat for Indonesian teenagers, including in the city of Jayapura, Papua province. The purpose of this study was to access Jayapura teenagers smoking behavior and knowledge including parents and other family members. The study was conducted on 78 respondents (grade 7, aged 11-14 years old), using cluster random sampling for selecting the public and private junior high school in Jayapura. The data collected was smoking behavior of respondents, parents and other family members (using self-reported questionnaire), and respondents' knowledge about the dangers of smoking (using tests with Cronbach's alpha 0.701). Data were analyzed descriptively and analytically using Chi-square, 95 % level of significant. The results showed 29.3 % of teenagers, 69.23 % of parents and 25.6 % of other family members were smokers, their knowledge was low (an average score of 60.81 out of 100), there was no significant statistical relationship between knowledge and smoking behavior among respondents (p = 0.079), and there is no significant relationship between teenagers behavior with the behavior of the parents (p = 0.609) and other family members (p = 0.578), 87 % of teenagers became smokers because there were individuals who smoke at home.