Driven by the era of big data and the development of artificial intelligence, potential traffic patterns can be obtained by analysing the numerous data. Metro has become an essential transport infrastructure and the passenger volume provides the basic support for the optimisation of the metro system. Thus, accurate forecasting of the volume is extremely required. In this study, a model for improving the accuracy and stability of metro passenger volume prediction named VMD‐TPE‐LightGBM (light gradient boosting machine) is proposed. The original dataset is firstly regrouped both in the station and chronological order while the time interval is reset as 10‐minute. Time features for extracting the hidden patterns are extracted by analysing the variation tendency of the passenger volume. For enhancing the precision, the variational mode decomposition algorithm is applied to decompose the original data series. Then each of the modes is regarded as the input of the LightGBM model, which are optimised by a tuning method named the tree of Parzen estimators and K‐fold cross‐validation. According to this process, the final forecasting results are acquired by reconstructing the predicted modes. The experimental results demonstrate that the proposed model performs superior to all the comparisons and has an impressive effect on short‐term metro passenger volume forecasting.
Objective: Type 2 diabetes mellitus complicated with microvascular diseases can be used as a model to study the relationship between bone health and the microvascular situation. Methods: A total of 2,170 patients with type 2 diabetes mellitus (1,188 postmenopausal females and 982 males aged ⩾50 years) were included in our cross-sectional study. These patients were grouped according to 24-hour urine protein level: Group I (<30 mg), Group II (30-299 mg) and Group III (≥300 mg). Bone mineral density of the lumbar spine, hip and femoral neck was evaluated by dual-energy X-ray absorptiometry. Fundus oculi photography for diabetic retinopathy and 24-h urine protein for diabetic nephropathy were used as markers of microangiopathy in type 2 diabetes mellitus. Characteristics of the patients and bone mineral density were compared. Multivariate analysis was used to study the association between bone mineral density and microangiopathy. Statistical analysis was performed using SPSS 20.0. p < 0.05 was considered statistically significant. Results: Group III had the lowest bone mineral density level in both genders. Multivariate analysis revealed that microangiopathy was negatively correlated with bone mineral density in females (lumbar: r = -0.522, p < 0.001; hip: r = -0.301, p = 0.010; femoral neck: r = -0.314, p = 0.009), but not in males, after adjustment for age, body mass index, hypertension, hyperlipidemia, diabetic status, hepatic function, kidney function, sex hormones and 25(OH) vitamin D.
Conclusion:These results demonstrate an independent negative correlation between microangiopathy and bone mineral density in postmenopausal female type 2 diabetes mellitus patients.
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