Research Articles

Machine learning models were developed to predict volatile organic compound and ethylene gas adsorption performance of freshness-preserving agents based on torrefied oak wood chips. Oak chips were torrefied at 350 °C for 20 min and processed into three particle sizes. A dataset of 39 experimental points was collected, comprising 8 input variables (particle size, torrefied wood content, commercial content, bulk density, compressed density, porosity, total content, and final bulk density) and 2 output variables (VOC and ethylene adsorption levels). Data augmentation techniques were applied to overcome dataset limitations. Three machine learning algorithms were implemented: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR). For ethylene adsorption, SVR achieved superior performance with R² = 0.934, RMSE = 5.06, and MAE = 1.997.). For VOC adsorption, RF demonstrated highest accuracy with R² = 0.962, RMSE = 1.11, and MAE = 0.845. Torrefied wood content was positively correlated with ethylene adsorption (r = 0.43). Porosity was negatively correlated (r = -0.76). Higher porosity gave reduced ethylene capture efficiency, consistent with a negative relationship between pore structure and adsorption. The effectiveness of machine learning was demonstrated in predicting gas adsorption performance. The work provides practical guidelines for designing torrefied wood-based freshness-preserving systems.

Acalypha indica, a plant used in traditional medicine, was evaluated for its phenolic composition and bioactivity. The methanolic extract of its aerial parts (stem and leaves) was analyzed using high-performance liquid chromatography (HPLC), identifying 17 phenolic compounds, including rutin (53.8 µg mL⁻¹), chlorogenic acid (53.3 µg mL⁻¹), gallic acid (36.3 µg mL⁻¹), and ferulic acid (33.3 µg mL⁻¹) as the primary constituents. These compounds correlated with the extract’s antioxidant activity, confirmed by the DPPH radical-scavenging assay, yielding an IC₅₀ of 6.8 µg mL⁻¹. The extract showed significant antimicrobial activity against Gram-positive bacteria, including Bacillus subtilis and Staphylococcus aureus, with inhibition zones exceeding that of Gentamycin. It also demonstrated moderate activity against Gram-negative bacteria, such as Salmonella typhi and Klebsiella pneumoniae, and antifungal activity against Candida albicans. Minimum inhibitory concentration (MIC) and bactericidal concentration (MBC) assays showed bactericidal effects at 7.8 µg mL⁻¹. Additionally, the extract inhibited biofilm formation and hemolysin production, suggesting anti-virulence potential. The Cyclooxygenase (COX) inhibition assays indicated anti-inflammatory effects (IC₅₀ = 11 µg mL⁻¹). Cytotoxicity tests on PC-3 prostate and SKOV-3 ovarian cancer cells revealed reductions in cell viability, with IC₅₀ values of 11.52 and 10.31 µg mL⁻¹, highlighting the therapeutic potential of Acalypha indica.

Northwestern Iran represents a significant data gap in the dendroclimatic network of West Asia, hindering a comprehensive understanding of regional hydroclimatic variability. This study aims to bridge this gap by constructing a robust tree-ring width chronology for Juniperus excelsa and evaluating its response to climatic extremes in the Hashtjin mountains of northwestern Iran. The authors analyzed 19 increment cores from 15 old-growth trees to develop a 110-year chronology (1898–2007). Statistical quality indicators confirmed a robust climatic signal. Analysis revealed that radial growth was primarily constrained by moisture availability, showing significant positive correlations with April and June precipitation and a significant negative correlation with April temperature, highlighting spring drought stress. Temporal instability in these climate-growth relationships was detected using moving window correlation. Four independent methods (IT, RGC, NW, zChron) identified major pointer years, with extreme droughts in 1907–1908 and 2001 consistently flagged. These extreme years show strong spatiotemporal coherence with historical drought records across West Asia and the Eastern Mediterranean, validating the chronology’s climatic sensitivity and underscoring regional synchronicity in major hydroclimatic events. The findings underscore the vulnerability of juniper ecosystems to warming-induced drought and provide a crucial proxy for filling paleoclimatic gaps in the region.

Fused deposition modeling (FDM) 3D printing technology was used to customize furniture risers to match furniture dimensions and children’s body sizes. The goal was to achieve a rapid, safe, and sustainable height adjustment solution for children’s furniture. Polyamide (PA) filament was chosen as the printing material. Compression tests were performed to investigate the properties of the 3D-printed PA models under varying process parameters, including layer thickness, infill density, and heated bed temperature. Experiments showed that as the layer thickness decreased, the infill density and the heated bed temperature increased. The compressive strength and compressive modulus of the PA model gradually increased, and the compressive properties improved. The optimized process parameters for compressive properties were: layer thickness (0.1 mm), infill density (90%), and heated bed temperature (90 °C). Using these parameters, this study completed the fabrication of furniture risers via FDM 3D printing. The 3D-printed furniture risers exhibited excellent compressive strength and surface finish, and they allowed for quick height adjustment to accommodate children’s varying body sizes. Moreover, the 3D printing approach itself was cost-effective and highly efficient. These benefits collectively highlight the application value of 3D printing for customized furniture components in promoting children’s healthy growth and saving resources.