签字笔墨迹的光老化及书写时间变化规律的研究

利用日光和紫外光对黑色签字笔字迹样本进行了光老化处理,通过区带毛细管电泳法分析了签字笔字迹墨水提取液,二极管阵列检测器记录了190～600 nm波长范围内的物质吸收峰,用电泳图中不同波长不同迁移时间的电泳峰面积之比对老化时间作图,得出了墨水中不同染料间含量的相对变化关系相对于老化时间的变化规律。同时也对自然老化的签字笔样品随书写时间的变化规律进行了研究。     

Optimal standby activation in m-out-of-n systems: A preventive approach

This article investigates an optimal preventive standby activation policy for an $m$-out-of-$n$ redundant system. We assume that for such a system, which starts operating at time $t=0$, a standby component will be activated at either the failure time of the system or at a predetermined time $\tau$, whichever occurs first. We first obtain the system reliability function under this switching policy as a function of $\tau$. Then, we investigate the optimal switching time $\tau$ so that the mean time to failure of the system is maximized. The results indicate that the existence of an optimal value of $\tau$ depends on the lifetime of the standby redundancy and its virtual age in the standby state. Some illustrative examples are presented to examine the theoretical outcomes.     

Metabolomics approach to growth-age discrimination in mountain-cultivated ginseng (Panax ginseng C. A. Meyer) using ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry

Mountain-cultivated ginseng is typically harvested after 10 years, while ginseng aged over 15 years is considered wild ginseng. This study aims to differentiate mountain-cultivated ginseng by age, as the fraudulent practice of selling low-aged cultivated ginseng disguised as high-aged one is damaging the market. In this study, LC-MS analyzed 98 ginseng samples, and multivariate statistical analysis identified patterns between samples to select influential components. Machine learning models were developed to identify ginseng samples of different ages. The untargeted metabolomic analysis clearly divided samples aged 4-20 years into three age groups. Twenty-two potential age-dependent biomarkers were discovered to differentiate the three sample groups. Three machine learning models were used to predict new samples, and the optimal model was selected. Some biomarkers could determine age phases according to the differentiation of mountain-cultivated ginseng samples. These biomarkers were thoroughly analyzed for variation trends. The machine learning models established using the screened biomarkers successfully predicted the age group of new samples.