Chiang Mai Journal of Science

     High infant mortality rate is still a worldwide problem and the situation is even worse in some developing countries or regions with political instability, low economic levels and epidemic diseases. In addition, some studies have shown that infant mortality rate affects fertility rate, so it is important to reduce the infant mortality rate when China's population has already started to grow negatively in 2022. Infant mortality rate is a discrete count data. This kind of data usually does not follow the basic assumption of normality, so the use of ordinary linear regression models on it may produce inconsistent or biased estimates. Double hierarchical generalized linear model (DHGLM) are a class of models that exist specifically for count data. They can model the mean and proportion parameters hierarchically and specify random effects for the mean and dispersion which is a good solution to the problem of underestimating standard errors due to overdispersion of the data. This paper proposes a double hierarchical generalized linear model combined with the integrated nested laplace approximation within markov chain monte carlo (MCMCINLA) to analyze the data of infant mortality rate in China. At the same time, a spatial lag term is added to the model to analyze the spatial correlation of the data. Finally, the influence of economic or medical factors on infant mortality rate is analyzed and some policy suggestions are given to reduce infant mortality rate according to the analysis results.

     A large amount of spent coffee grounds has been generated from the coffee industry; therefore, much research has been conducted to investigate their applications. For textiles, nowadays an eco-trend has been receiving a greater attention, especially in the textile chemical processing, i.e. dyeing. Natural dye is an option for safer textiles but their performance is still under concerns. In this study, the utilization of a food waste, spent coffee grounds, as a raw material for natural dyeing was investigated. The dye extracted from spent coffee grounds (SCG dye) was analyzed for their chemical composition. The dyeing properties of SCG dye were conducted on cotton, silk, and nylon fabrics. Optimized dyeing performance of SCG dye on the fabrics was investigated at various dyebath pHs. An appropriate dyebath pH for dyeing SCG dye was pH 4 for all the fabric types. SCG dye showed superior dyeing on silk followed by nylon, while it dyed cotton poorly. Non-metal, bio-mordant-enhanced dyeing performance of SCG dye was achieved by post-mordanting the fabrics with tannin from cassava leaves (CL tannin). CL tannin apparently improved the color strength of the dye on silk fabric. The color fastness to washing, crocking, and light of the dyed fabrics in the absence and presence of CL tannin was also reported. SCG dye could enhance the antibacterial properties against S. Aureus for cotton, silk, and nylon, whereas it could not help prevent the growth of E. Coli at the color depth studied. It is evident that CL tannin present on the dyed fabrics improved the antibacterial properties of the dyed fabrics. All of the SCG dyed fabrics displayed excellent UV protection (UPF 50+). From this research, it points out that the SCG dye is a preferable natural dye for further development of eco-textiles with an almost entirely eco-friendly dyeing process.