This study was conducted to reset the withdrawal time (WT) for amoxicillin (AMX) in pigs as a part of positive list system (PLS) program introduction. Forty-two healthy pigs were orally administered with AMX at doses of 10 mg/kg body weight (BW) (AMX-1, n=20) and 20 mg/kg BW (AMX-2, n=20), twice daily for 5 days, respectively. After the treatment, tissue samples were collected from four pigs at 1, 3, 5, 7 and 14 days post-administration, respectively. Based on a previously established analysis method, residual AMX concentrations in pig tissues were determined using LC-MS/MS. In both AMX-1 and AMX-2 groups, AMX levels in all tissues except fat was below the limit of quantification (LOQ) at one day after the final administration. According to the European Medicines Agency’s guideline on determination of withdrawal periods, the withdrawal periods for AMX-1 and AMX-2 in fat tissue were established as 0 and 2 days, respectively. In conclusion, the estimated WT of AMX in edible tissues of pigs is shorter than the current WT recommendation of 5 days for AMX.

Visfatin, an adipokine secreted by cells, is crucial for intracellular nicotinamide adenine dinucleotide+ biosynthesis. Extracellularly, visfatin plays diverse roles in inflammatory conditions, including obesity, which is closely linked to osteoclastogenesis. We previously showed that visfatin enhances receptor activator of nuclear factor kappa-B ligand (RANKL)-induced osteoclastogenesis in bone marrow-derived macrophages. However, its enzymatic activity during this process is poorly understood. Here, we investigated visfatin’s effects on RANKL-induced osteoclast differentiation. Our results demonstrate that visfatin promotes this differentiation, an effect inhibited by FK866, an inhibitor of visfatin’s enzymatic activity. Furthermore, FK866 also inhibited RANKL-induced osteoclast differentiation. These findings suggest that inhibiting visfatin’s enzymatic activity modulates osteoclast differentiation. Thus, visfatin plays an important role in osteoclastogenesis, both intracellularly and extracellularly, and FK866 has therapeutic potential for diseases characterized by imbalanced osteoclast formation, such as osteoporosis and periodontitis.

Effective cooling strategies are critical for cultivating high-quality ornamental plants during the summer. The fan-and-pad cooling system reduces greenhouse temperatures by drawing air through wet pads, which humidify and cool the air, aided by fans on the opposite side. However, the paper-based pads (corrugated cellulose) used in this system have limited durability and degrade with prolonged use. Nanocomposite hydrogels, with their polymer-based structure, can absorb and retain moisture through swelling, presenting a promising alternative. This study examines the application of nanocomposite hydrogels, focusing on their hygroscopic properties and cooling efficiency under various temperatures and wind speeds. When treated with lithium chloride solutions at 25%, 50%, 75%, and 100% saturation, higher LiCl concentrations reduced weight but increased swelling capacity. Optimal cooling effects were achieved with wind speeds of 1.0 m/s at 25°C and 1.5 m/s at 35°C, with greater efficiency observed at lower wind speeds. These findings suggest that integrating nanocomposite hydrogels into cooling pads could enhance durability and reduce maintenance compared with conventional paper pads.

We determined complete mitochondrial genome of Erpobdella sp. isolated in Korea. The circular mitochondrial genome of Erpobdella sp. is 15,469 bp long, which is longer than other three complete mitochondrial genomes of Erpobdella species. It includes 13 protein-coding genes, two ribosomal RNA genes, and 22 transfer RNAs. Its GC ratio is 30.2%. Phylogenetic trees show that our mitochondrial genome is clustered in Erpobdellidae clade.

In story writing, interjections are used in dialogue to enhance the emotional tone of the text. However, crafting realistic dialogues that effectively incorporate interjections can be a challenging task for young learners with developing writing skills. This study examines how young learners utilize interjections in their story writing. The study analyzed the narratives of 242 students from three different English proficiency groups: lower and higher level EFL elementary school students and native English speakers in seventh to twelfth grade. The analysis aimed to understand the relationship between interjection frequency and writing qualities. The findings revealed a negative correlation between the occurrence of interjections and both content quality and vocabulary diversity. Additionally, comparisons across proficiency groups indicated that certain types of interjections were more prevalent in specific groups. These results suggest that learners should use interjections judiciously in story writing. Although interjections may seem peripheral, they warrant closer attention as they can subtly detract from writing qualities.

This study aims to identify crisis signs in small and medium enterprise (SME)-concentrated regions and establish measures to prevent economic recession and normalize regional economies through proactive responses. To achieve this, we investigated and analyzed the crisis status and outlook of companies located in Jeonbuk, their detailed management conditions, management issues by industry, difficulties in business operations, and policy demands. Out of 4,144 SMEs in Jeonbuk's concentrated areas, 270 companies responded to the survey. The results showed that 60% of the responding companies perceived their current management situation as being in a state of crisis. However, the outlook for the next quarter and the following year is expected to improve. Notably, compared to manufacturing companies, non-manufacturing firms responded that their crisis situation in the next quarter would not improve and expected the crisis to persist. In terms of detailed business conditions, regardless of the distinction between manufacturing and non-manufacturing sectors, all aspects of the survey, including domestic sales, export sales, operating profit, financial status, and the number of employees, indicated better prospects for the next quarter and the following year compared to the current quarter. The study's findings suggest that companies in SME-concentrated areas of Jeonbuk are relatively accurate in recognizing the crisis situation of their own businesses and operating markets. Additionally, the companies responded that crisis monitoring is necessary. Differences in difficulties faced by the manufacturing and non-manufacturing sectors imply the need for industry-specific financial support programs. Based on the survey results, we propose financial support projects tailored to the manufacturing and non-manufacturing sectors, considering the degree of market competition. For more precise research, future studies will involve extracting larger samples and conducting a detailed analysis by subdividing manufacturing sectors (e.g., food, metal) and non-manufacturing sectors (e.g., agriculture, design).

This study develops a machine learning-based tool life prediction model using spindle power data collected from real manufacturing environments. The primary objective is to monitor tool wear and predict optimal replacement times, thereby enhancing manufacturing efficiency and product quality in smart factory settings. Accurate tool life prediction is critical for reducing downtime, minimizing costs, and maintaining consistent product standards. Six machine learning models, including Random Forest, Decision Tree, Support Vector Regressor, Linear Regression, XGBoost, and LightGBM, were evaluated for their predictive performance. Among these, the Random Forest Regressor demonstrated the highest accuracy with R2 value of 0.92, making it the most suitable for tool wear prediction. Linear Regression also provided detailed insights into the relationship between tool usage and spindle power, offering a practical alternative for precise predictions in scenarios with consistent data patterns. The results highlight the potential for real-time monitoring and predictive maintenance, significantly reducing downtime, optimizing tool usage, and improving operational efficiency. Challenges such as data variability, real-world noise, and model generalizability across diverse processes remain areas for future exploration. This work contributes to advancing smart manufacturing by integrating data-driven approaches into operational workflows and enabling sustainable, cost-effective production environments.