Background: Flexible flatfoot impairs gait and posture by weakening arch support, potentially leading to musculoskeletal dysfunction. Strengthening exercises, such as the short foot exercise (SFE), have shown promise in correcting this condition. Objectives: This study aimed to investigate the effects of SFE with visual feedback on medial arch height and foot function in adults with flexible flatfoot. Design: Experimental research. Methods: Adults diagnosed with flexible flatfoot were randomly assigned to either an experimental or control group. The experimental group performed SFE with visual feedback, whereas the control group performed the same exercises without feedback. Both groups trained three times per week for five weeks. Outcome measures included the Navicular Drop Test (NDT), YBalance Test (YBT), and Tetrax postural analysis. Results: In the NDT, both groups showed significant improvements (P<.05), while in the YBT, only the experimental group showed a significant improvement (P<.05). In contrast, there were no significant changes in the Weight Distribution Index (WDI) and Stability Test (ST) areas of the Tetrax system in either group (P>.05). Conclusion: SFE effectively improved arch height regardless of visual feedback, though only the visual feedback group showed significant improvements in dynamic balance. However, between-group differences were not statistically significant, suggesting that visual feedback provides subtle rather than substantial additional benefits. Further research with larger samples is needed to establish the clinical value of adding visual feedback to SFE protocols.

Freshwater bivalves contribute to key ecological functions in lake ecosystems, yet their cryptic and benthic lifestyles often hinder detection through conventional surveys. In this study, we applied environmental DNA (eDNA) metabarcoding to assess the diversity and distribution of unionid bivalves in six lakes across Republic of Korea. Water samples were collected from three sampling strategies-Center Surface, Center Mix, and Waterside Surface-and processed using 16S rDNA-targeted primers followed by high-throughput sequencing. A total of four unionid species (Cristaria plicata, Sinanodonta lauta, Unio (Nodularia) douglasiae, and Anodonta woodiana) were detected across 18 sampling points. Notably, eDNA successfully identified unionid presence in all lakes, even where conventional surveys failed to observe individuals. Among the sampling strategies, Center Mix exhibited the highest values for Shannon and Simpson indices as well as ASV richness. Waterside Surface samples generally showed lower diversity and detection frequency. A Venn diagram of ASV occurrences revealed three ASVs shared across all sampling strategies and one unique ASV found only in Center Mix. These results indicate that sampling location significantly affects detection sensitivity and diversity representation in eDNA-based bivalve monitoring. Combined application of Center Mix and Center Surface strategies may enhance both detection efficiency and species diversity coverage in lentic environments.

Phytoplankton play a vital role as primary producers in freshwater ecosystems, contributing to the nutrient cycle, energy flow, and ecological stability. To accurately assess phytoplankton diversity and community composition, this study compared traditional microscopy and environmental DNA (eDNA) metabarcoding in six small lakes located in the Han, Geum, and Nakdong River basins in Korea. eDNA analysis identified 268 species from 161 genera, approximately 2.4 times higher than microscopy, which detected 113 species from 68 genera. The eDNA data were dominated by picocyanobacteria such as Synechococcus and Cyanobium, while microscopy primarily revealed larger taxa, including Stephanodiscus and Scenedesmus. Nonmetric multidimensional scaling (NMDS) based on Bray-Curtis similarity showed clear separation between the two methods, with average similarity values of 0.0326 (1st survey) and 0.0221 (2nd survey) at the species level. Only 6.8% of the 429 total species were commonly detected by both methods, while overlap at the genus level was 18.8%. Spatial heterogeneity in phytoplankton communities based on eDNA was also evident depending on the sampling location, with the centre of the surface showing the highest species richness and overlap, suggesting its suitability for biodiversity monitoring. These findings demonstrate the high resolution and sensitivity of eDNA metabarcoding in capturing phytoplankton diversity and highlight its complementary role in existing biomonitoring programmes. Further improvements in the quantitative reliability of eDNA-based assessments will require efforts such as copy number normalisation, methodological standardisation, and refinement of reference databases.

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.