Exploring Malaysian schoolchildren's perception of the advantages and disadvantages of the ToothPoly board game: a qualitative study.

The effectiveness of children's oral health education (OHE) is determined by the appropriateness of the educational materials used, which can influence their attitude towards oral health. However, there is a lack of studies exploring the benefits of OHE materials from the perspective of schoolchildren. This study aimed to explore schoolchildren's opinions on the newly developed ToothPoly board game as an OHE tool. A qualitative approach using focus group discussions (FGDs) was conducted among 44 schoolchildren aged 12 years old from a public school in Malaysia. Convenience sampling was employed to recruit the schoolchildren. The ToothPoly board game was playtested and FGDs were conducted after the playtesting session ended. Data collection and analyses were performed concurrently until data saturation was reached. The data were transcribed and coded using Atlas.Ti software version 9.1.3 followed by the framework method analysis. Mixed opinions were observed among the schoolchildren with a majority expressing favourable opinions on the advantages of the ToothPoly board game as an OHE tool. Five themes emerged from the advantages aspect, i.e., fun and enjoyable, promote focus, attention and oral health-related learning, attractive board game features, and enhance peer interaction. Meanwhile, two themes emerged that were related to the disadvantages of the board game, i.e., "competition with online games and media" and "not practical for a large group activity". The findings showed that the ToothPoly board game was perceived as a useful, interactive, and enjoyable tool to learn about oral health in small groups. The findings of the study highlight the importance of tailoring OHE activities to fulfil the needs of specific target groups to ensure its acceptance and future success.

A New Local Fractional Entropy-Based Model for Kidney MRI Image Enhancement.

Kidney image enhancement is challenging due to the unpredictable quality of MRI images, as well as the nature of kidney diseases. The focus of this work is on kidney images enhancement by proposing a new Local Fractional Entropy (LFE)-based model. The proposed model estimates the probability of pixels that represent edges based on the entropy of the neighboring pixels, which results in local fractional entropy. When there is a small change in the intensity values (indicating the presence of edge in the image), the local fractional entropy gives fine image details. Similarly, when no change in intensity values is present (indicating smooth texture), the LFE does not provide fine details, based on the fact that there is no edge information. Tests were conducted on a large dataset of different, poor-quality kidney images to show that the proposed model is useful and effective. A comparative study with the classical methods, coupled with the latest enhancement methods, shows that the proposed model outperforms the existing methods.