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  1. Effect of Surface Treatments on the Bond Strength to Turkom-Cera All-ceramic Material
    Razak AA, Abu-Hassan MI, Al-Makramani BM, Al-Sanabani FA, Al-Shami IZ, Almansour HM
    J Contemp Dent Pract, 2016 Nov 01;17(11):920-925.
    PMID: 27965501
    AIM: The aim of this study was to evaluate the effect of surface treatments on shear bond strength (SBS) of Turkom-Cera (Turkom-Ceramic (M) Sdn. Bhd., Puchong, Malaysia) all-ceramic material cemented with resin cement Panavia-F (Kuraray Medical Inc., Okayama, Japan).

    MATERIALS AND METHODS: Forty Turkom-Cera ceramic disks (10 mm × 3 mm) were prepared and randomly divided into four groups. The disks were wet ground to 1000-grit and subjected to four surface treatments: (1) No treatment (Control), (2) sandblasting, (3) silane application, and (4) sandblasting + silane. The four groups of 10 specimens each were bonded with Panavia-F resin cement according to manufacturer's recommendations. The SBS was determined using the universal testing machine (Instron) at 0.5 mm/min crosshead speed. Failure modes were recorded and a qualitative micromorphologic examination of different surface treatments was performed. The data were analyzed using the one-way analysis of variance (ANOVA) and Tukey honestly significant difference (HSD) tests.

    RESULTS: The SBS of the control, sandblasting, silane, and sandblasting + silane groups were: 10.8 ± 1.5, 16.4 ± 3.4, 16.2 ± 2.5, and 19.1 ± 2.4 MPa respectively. According to the Tukey HSD test, only the mean SBS of the control group was significantly different from the other three groups. There was no significant difference between sandblasting, silane, and sandblasting + silane groups.

    CONCLUSION: In this study, the three surface treatments used improved the bond strength of resin cement to Turkom-Cera disks.

    CLINICAL SIGNIFICANCE: The surface treatments used in this study appeared to be suitable methods for the cementation of glass infiltrated all-ceramic restorations.

  2. Fulltext Improving Water Quality Index Prediction Using Regression Learning Models
    Mohd Zebaral Hoque J, Ab Aziz NA, Alelyani S, Mohana M, Hosain M
    Int J Environ Res Public Health, 2022 Oct 21;19(20).
    PMID: 36294286 DOI: 10.3390/ijerph192013702
    Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties. Analyzing the trend and pattern of these parameters enables the prediction of the water quality so that proactive measures can be made by relevant authorities to prevent water pollution and predict the effectiveness of water restoration measures. Machine learning regression algorithms can be applied for this purpose. Here, eight machine learning regression techniques, including decision tree regression, linear regression, ridge, Lasso, support vector regression, random forest regression, extra tree regression, and the artificial neural network, are applied for the purpose of water quality index prediction. Historical data from Indian rivers are adopted for this study. The data refer to six water parameters. Twelve other features are then derived from the original six parameters. The performances of the models using different algorithms and sets of features are compared. The derived water quality rating scale features are identified to contribute toward the development of better regression models, while the linear regression and ridge offer the best performance. The best mean square error achieved is 0 and the correlation coefficient is 1.
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