Kangwon National University

This study investigated the effects of four LED light qualities, red+blue+far-red (WRS-LED), blue+red (BR-LED), blue (B-LED), and red (R-LED), and exogenous L-glutamic acid at 10 ppm on the growth and quality of red mustard spinach (Brassica rapa var. perviridis) cultivated in a plant factory using a recirculating deep-flow hydroponic system. Plants were exposed to four LED light quality treatments at 180 ± 10 μmol·m−2·s−1 PPFD for 28 days after transplanting. L-glutamic acid at 10 ppm was applied once to the recirculating nutrient solution 15 days after transplanting, resulting in 13 days of exposure prior to final harvest on day 28. All growth and quality parameters were measured at the final harvest after 28 days of cultivation. WRS-LED promoted the greatest biomass production. Additionally, vitamin C content, DPPH radical scavenging activity, and total phenolic content were highest under BR-LED and B-LED conditions. Notably, under B-LED, L-glutamic acid treatment increased total phenolic content to approximately twice that of the control. Leaf redness, expressed as Hunter a* values, was observed exclusively under BR-LED. Principal component analysis revealed that LED light quality was the primary determinant of treatment responses, with growth-related traits associated with WRS-LED and R-LED, and quality-related traits with B-LED and BR-LED. Overall, BR-LED combined with L-glutamic acid represents the most suitable treatment for red mustard spinach cultivation in plant factories, achieving a favorable balance between growth and nutritional quality.

Mild cognitive impairment (MCI) represents a high-risk stage for dementia, yet limited non-pharmacological interventions are accessible. Although nature-based interventions have potential cognitive benefits, empirical validation for MCI populations remains limited. This study aims to address this gap. Fifty older adults with MCI were recruited from a psychiatric outpatient clinic and local dementia centers. The intervention group included 25 participants who received a four-session nature-based intervention over 4 weeks, while controls, matched for age and sex, received no intervention. Pre- and post-tests assessed cognitive, emotional, physiological, and physical functions. Mini-Mental State Examination (MMSE) scores demonstrated a significant time×group interaction [F(1,45)=10.226, p=0.003]. The nature-based intervention group showed that MMSE scores increased significantly (t=-2.270, p=0.034). In contrast, the control group exhibited a significant decline (t=2.262, p=0.034). No significant time and group interactions were found for emotional, physiological, or physical outcomes. This short-term, nature-based intervention yielded cognitive benefits in older adults with MCI, which supported its feasibility as an accessible, non-pharmacological intervention. Further longitudinal randomized controlled trials should confirm sustained effects across multiple domains.

Type 2 Diabetes (T2D) is characterized by the toxic aggregation of human islet amyloid polypeptide (hIAPP or amylin) within pancreatic β-cells. IAPP is also a neuropancreatic hormone that plays a significant role in Alzheimer's disease (AD) by co-depositing with amyloid-beta (Aβ) and Tau, supporting the Type 3 Diabetes (T3D) hypothesis. Soluble IAPP accelerates Aβ aggregation through cross-seeding and causes neurotoxicity by impairing the blood-brain barrier and activating neuroinflammation. Melatonin inhibits these processes by disrupting hydrophobic interactions in both hIAPP and Aβ, preventing the formation of toxic β-sheet structures. Furthermore, melatonin promotes amyloid clearance via the glymphatic and lymphatic systems, protects neurons from oxidative damage, and reduces Tau hyperphosphorylation. This suggests that melatonin serves as a promising multitarget therapeutic agent for both metabolic and neurodegenerative disorders by modulating structural protein transformations.

Background: The rapid growth of e-commerce has intensified the need for packaging strategies that reduce logistics costs and environmental impact. Traditional box recommendation methods select the best-fitting box from a fixed set of options, which limits their ability to minimize unused space and total costs. Methods: This study formulates the Shipping Box Configuration Problem (SBCP), which aims to determine an optimal set of box types and dimensions for multi-product orders. To solve this problem, we propose a Particle Swarm Optimization (PSO)-based heuristic that dynamically designs box configuration rather than selecting from predefined sizes. Results: The proposed method is evaluated using real order data from two South Korean e-commerce companies with different product characteristics and existing box configurations. Computational results show that the PSO-based approach reduces total packaging and shipping costs and improves space utilization compared to current box configurations. The analysis also indicates that increasing the number of box types and reducing safety ratios generally lead to cost savings, although these effects must be balanced against operational complexity. Conclusions: The results suggest that adaptive box configuration design can improve both economic efficiency and environmental performance, providing practical guidance for e-commerce logistics managers seeking to optimize packaging strategies under operational constraints.

Subseasonal forecasts, which predict weather patterns from weekly up to seasonal timescales, are crucial to minimize the adverse impacts of extreme weather events, such as heatwaves and droughts, on ecosystems and society. However, forecast skill at subseasonal lead times remains limited, as the chaotic nature of the atmosphere reduces the usefulness of the information contained in atmospheric initial conditions for increasing lead times. In contrast, land surface states, including soil moisture and vegetation anomalies, evolve more slowly and retain memory over weeks, allowing them to persist across subseasonal timescales and making them a potentially important source of predictability. Despite this, most operational weather forecast models represent only the mean seasonal cycle of land conditions, because accurately incorporating land surface anomalies remains challenging and can degrade model performance.In order to address this situation, we develop a prototype of a hybrid weather prediction model to forecast near-surface temperature and surface soil moisture and related extremes. The model leverages the flexibility of deep learning to build on (i) satellite-based land surface observations, (ii) short-range forecasts from the Integrated Forecasting System to inform the model with physically consistent atmospheric evolution, and (iii) previous meteorological conditions sourced from reanalysis data. First results suggest that land surface anomalies exert a stronger influence during extreme conditions, when land memory persists, whereas under average conditions their influence is more evenly shared with atmospheric anomalies. Our study provides a benchmark for integrating land surface information into hybrid forecasting systems and highlights pathways to improve subseasonal prediction and early warning systems.

Elucidating fault zone processes during long-term seismic cycles is critical for mitigating earthquake hazards in intraplate regions. We investigated the hydro-mechanical evolution of a strike-slip branch of the Yangsan Fault, SE Korea, which bounds Triassic and Jurassic granites. By integrating multiscale observation with high-velocity rotary shear experiments and XRD, we characterized the fault architecture, which consists of a