The Indonesian National Assessment set by the government is in line with the Program for International Student Assessment (PISA) assessment which includes students’ scientific literacy skills for a 21st century education succeeds. This study determined the increase in students’ scientific literacy skills through ethnoscience, science, technology, engineering, mathematics or ethno-STEM learning assisted by RE-STEM App. A quasi-experimental design with the type of one-group pretest-posttest design was used to obtain data from the treatment. There were 102 students involved by purposive sampling technique from a 338 population of grade 8 students in a rural secondary school in Central Java, Indonesia. RE-STEM App involves four themes of Indonesian cultures as learning resources, namely tarutu, tin-telephone, calung, and bird whistle. This study revealed that students’ scientific literacy skills increased by 61.33% in the medium category in general, also in each aspect. Research data indicate that the RE-STEM App has supported teachers to improve students’ scientific literacy skills. By applying ethno-STEM through RE-STEM App, teachers can help students to investigate the science concepts in ethnoscience. This strategy proved that ethnoscience aspect can be an important factor in developing students’ scientific literacy skills.

Medical image segmentation, especially in chest X-ray (CXR) analysis, encounters substantial problems such as class imbalance, annotation inconsistencies, and the necessity for accurate pathological region identification. This research aims to improve the precision and clinical reliability of pulmonary abnormality segmentation by developing NCT-CXR, a framework that combines anatomically constrained data augmentation with expert-guided annotation refinement. NCT-CXR applies carefully calibrated discrete-angle rotations (±5°, ±10°) and intensity-based augmentations to enrich training data while preserving spatial and anatomical integrity. To address label noise in the NIH Chest X-ray dataset, we further introduce a clinically validated annotation refinement pipeline using the OncoDocAI platform, resulting in multi-label pixel-level segmentation masks for nine thoracic conditions. YOLOv8 was selected as the segmentation backbone due to its architectural efficiency, speed, and high spatial accuracy. Experimental results show that NCT-CXR significantly improves segmentation precision, especially for pneumothorax (0.829 and 0.804 for ±5° and ±10°, respectively). Non-parametric statistical testing (Kruskal–Wallis, H = 14.874, p = 0.0019) and post hoc Nemenyi analysis (p = 0.0138 and p = 0.0056) confirm the superiority of discrete-angle augmentation over mixed strategies. These findings underscore the importance of clinically constrained augmentation and high-quality annotation in building robust segmentation models. NCT-CXR offers a practical, high-performance solution for integrating deep learning into radiological workflows.

Medical image segmentation, especially in chest X-ray (CXR) analysis, encounters substantial problems such as class imbalance, annotation inconsistencies, and the necessity for accurate pathological region identification. This research introduces NCT-CXR, a robust framework that enhances semantic segmentation in CXR images using an improved coordinate-geometric transformation strategy. NCT-CXR integrates carefully calibrated geometric transformations with intensity-based augmentations, ensuring spatial accuracy throughout the augmentation process. The framework was evaluated on the NIH Chest X-ray dataset comprising 1,061 images across nine pathological categories. NCT-CXR has four different coordinate transformation models, i.e. discrete rotations at (-10°, +10°), discrete rotations at (-5°, +5°), and mixed rotation augmentation. Semantic segmentation was performed using YOLOv8 with optimized hyperparameters. Non-parametric statistical analysis using Kruskal-Wallis test revealed significant differences in precision metrics (H= 14.874, p = 0.001927), while other performance metrics remained stable. Subsequent Nemenyi post-hoc analysis demonstrated that discrete-angle rotations at (-5°, +5°) and (-10°, +10°) significantly outperformed mixed rotations (p = 0.013806 and p = 0.005602 respectively). These models achieved particularly high precision in pneumothorax detection (0.829 and 0.804 respectively), emphasizing the effectiveness of controlled geometric transformations for conditions with well-defined anatomical boundaries. These findings demonstrate the efficacy of NCT-CXR in producing clinically relevant segmentation outcomes and underscore the importance of augmentation design in pathology-specific model performance. Future work will explore the generalizability of this approach across diverse imaging modalities and its applicability to a broader spectrum of thoracic conditions.