RESUMO
The intensification of market competition makes refined operation management become the focus of attention of major manufacturers. As an important branch of artificial intelligence (AI), machine learning (ML) plays a key role in it, and has its application prospect in various systems. Based on this situation, this paper takes vending machines as the research object. On the one hand, the product classification model of vending machine is constructed based on decision tree algorithm. On the other hand, based on neural network (NN), the sales forecast model of vending machines is built. Finally, based on the above research, the theoretical framework of decision support system (DSS) for vending machines is constructed. The research shows that: (1) The accuracy of C4.5 algorithm can reach 87 % at the highest and 68 % at the lowest. The accuracy of the improved C4.5 algorithm can reach 87 % at the highest and 67 % at the lowest, with little difference between them. (2) The maximum running time of the improved C4.5 algorithm is about 5500 ms, and the minimum is close to 1 ms. In addition, the running time of all seven datasets is better than that of the unmodified algorithm. (3) When the back propagation neural network (BPNN) is used to forecast the sales of vending machines, the curve of the predicted data basically coincides with the curve of the actual data, which shows that its accuracy is high. This paper aims to build a convenient and secure DSS by taking vending machines as an example. In addition, this paper also uses reinforcement learning to optimize the research methods of this paper. It can further optimize the performance and efficiency of vending machines and provide better service experience for customers. Meanwhile, the use of reinforcement learning can make the whole system more intelligent and adaptive to better cope with the changing market environment.
RESUMO
OBJECTIVES: To determine the 99th percentile upper reference limit (URL) of high-sensitivity cardiac troponin I (hs-cTnI) in a healthy population in Xinjiang, China, and investigate the impact of ethnicity, sex, and age on this limit. DESIGN AND METHODS: From September 2018 to March 2022, 5,090 Han and Uyghur adults aged 20-79 years were recruited. After questionnaire screening, 2,970 participants with physical and/or laboratory normality were enrolled. Participants recruited between September 2018 and October 2021 (2,109/2,970) were evaluated by ARCHITECTi2000 to determine the 99th percentile URL of hs-cTnI. The results were then validated in 861/2,970 participants recruited from November 2021 to March 2022. A criterion of ≤ 10% of test results falling outside the original determined value was used to determine whether the newly established reference intervals were valid. RESULTS: The hs-cTnI concentration was higher among Uyghurs than among Han participants (p < 0.001). The 99th percentile URLs were 17.52 ng/L for all participants, 18.96 ng/L for Uyghur, and 16.93 ng/L for Han. Hs-cTnI concentration was also correlated with sex and age. In the Han and Uyghur groups, male participants had a higher hs-cTnI concentration than female participants (p < 0.001); the 99th percentile URLs of hs-cTnI among male and female participants were 17.80 vs. 13.67 ng/L and 19.47 vs. 16.52 ng/L, respectively. Stratified by age, hs-cTnI concentrations were higher in participants aged > 60 years than in those of other age categories (p < 0.001), in both the Han and Uyghur groups. Finally, <2% of these test results exceeded the newly established reference, validating the results. CONCLUSIONS: This study established the 99th percentile URLs of hs-cTnI in the Xinjiang. Ethnicity and sex influence the value and should be considered.
