Salivary a-amylase (sAA) is responsible for the 'pre-digestion' of starch in the oral cavity and accounts for up to 50 % of salivary protein in human saliva. An accumulating body of literature suggests that sAA is of nutritional importance; however, it is still not clear how sAA is related to individual's nutritional status. Although copy number variations (CNV) of the salivary amylase gene (AMY1) are associated with variation in sAA levels, a significant amount of sAA variation is not explained by AMY1 CNV. To measure sAA responses to gustatory stimulation with citric acid, we used sAA ratio (the ratio of stimulated sAA levels to those of resting sAA) and investigated acute sAA responses to citric acid in children with normal (Normal-BMI, n 22) and low (Low-BMI, n 21) BMI. The AMY1 gene copy number was determined by quantitative PCR. We, for the first time, demonstrated attenuated acute sAA responses (decreased sAA ratio) to gustatory stimulation in Low-BMI (thinness grade 3) children compared with the Normal-BMI children, which suggest that sAA responses to gustatory stimulation may be of nutritional importance. However, child's nutritional status was not directly related to their resting or stimulated sAA levels, and it was not associated with AMY1 gene copy number. Finally, AMY1 CNV might influence, but did not eventually determine, sAA levels in children.
In this paper, a mathematical model is developed to investigate the economic e ¶ ects of set-up time reduction. In this model, the concept of product life cycle and continuous demand are included, with the objective of minimizing the total relative cost while demand is variable over time. Also, budget constraints are added to describe the conditions of limited investment. Following the suggested procedure of deciding investment in set-up time reduction, a manager can conclude if set-up time reduction is economically feasible and allocate the investment optimally. Finally, an example about the declining market demand is provided to demonstrate the application of the model.
NomenclatureThe following is a list of notations used throughout this paper. The numerical value which follows a variable is used for calculations in section 4. A the storage space per pallet (ˆ1:2 m 2 /pallet) c a cost per square meter of storage per year (ˆUS$ 3 000 per square metre per year) c f pallet usage cost per pallet per year (ˆUS$ 200/pallet/year) c r rework cost per part (ˆUS$ 5/part) c s scrap cost per part (ˆUS$ 9/part) D…t † demand rate, D…t † 0 H inventory carrying cost for each part per year (ˆUS$ 0.9/part/year) ICC inventory carrying cost
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