This study presents a comprehensive analysis of the chemical composition and bioactive properties of six newly cultivated varieties of pigmented and non-pigmented rice: Neunkeunheukchal (NKH), Neunkeunssal, Heukjagwang 709 (H709), Heukjagwang 1601, Hongjinju, and Hongchapssal (HCS). This study aims to characterize the chemical information and the industrial potential of lesser-known rice varieties. Significant differences were observed in the levels of phenolic compounds, flavonoids, γ-oryzanol, and free amino acids among these varieties, correlating with their antioxidant capacities. Among these varieties, NKH consistently exhibited the highest total phenolic, flavonoid, and γ-oryzanol contents, along with the strongest radical-scavenging activities, indicating its potential as a valuable functional food ingredient. Additionally, H709 and HCS demonstrated significant antioxidant activities, highlighting their potential roles in health-promoting applications. These findings highlight the potential of these varieties for functional food and nutraceutical applications. Future research should investigate the effects of cultivation conditions and processing methods on bioactive compound levels while conducting clinical trials to validate these health benefits in human models. The findings suggest that NKH, given its rich bioactive profile, may be particularly effective in managing oxidative stress and associated chronic diseases. Furthermore, the γ-oryzanol contents, the highest in NKH, highlight its potential for metabolic health benefits. This study lays the groundwork for future investigations into the development of functional foods and nutraceuticals derived from the unique characteristics of pigmented and non-pigmented rice varieties.

Single Image Super-Resolution (SISR) reconstructs high-resolution images from low-resolution inputs, enhancing image details. While Vision Transformer (ViT)-based models improve SISR by capturing long-range dependencies, they suffer from quadratic computational costs or employ selective attention mechanisms that do not explicitly focus on query-relevant regions. Despite these advancements, prior work has overlooked how selective attention mechanisms should be effectively designed for SISR. We propose SSCAN, which dynamically selects the most relevant key-value windows based on query similarity, ensuring focused feature extraction while maintaining efficiency. In contrast to prior approaches that apply attention globally or heuristically, our method introduces a query-aware window selection strategy that better aligns attention computation with important image regions. By incorporating fixed-sized windows, SSCAN reduces memory usage and enforces linear token-to-token complexity, making it scalable for large images. Our experiments demonstrate that SSCAN outperforms existing attention-based SISR methods, achieving up to 0.14 dB PSNR improvement on urban datasets, guaranteeing both computational efficiency and reconstruction quality in SISR.