Analyzing Capacity Utilization and Travel Patterns of Chinese High-Speed Trains: An Exploratory Data Mining Approach

Train capacity utilization (TCU), usually represented by passenger load factor (PLF), is a critical measure of effectiveness for rail operation. In literature, efforts are usually made to improve capacity utilization by optimizing rail operation and management strategies. Comparably little attention is paid to analyzing the factors that affect TCU and to understanding the behavioral patterns behind it. This paper applies exploratory data mining techniques to a 3-month long real world train operation data of the Beijing-Shanghai High-Speed Railway. Principal component analysis (PCA) is conducted to find the principal components that can efficiently represent the collected data. Clustering techniques are then applied to understand the unique characteristics that affect PLF and the travel pattern. The findings can be further used to guide train operation planning and facilitate better decision-making

Sensitivity Analysis of Runoff and Wind with Respect to Yellow River Estuary Salinity Plume Based on FVCOM

In 2020, Yellow River runoff was more than twice as much as past years, and the proportion of strong winds was also higher than that in past years, which will inevitably lead to a change in salinity plume distribution in the Yellow River Estuary and Laizhou Bay. Based on FVCOM numerical modelling, this paper presents the spatial salinity distribution and dispersion of the Yellow River Estuary and Laizhou Bay during the wet and dry seasons in 2020. We used data from six tidal and current stations and two salinity stations to verify the model, and the results showed that the model can simulate the local hydrodynamic and salinity distribution well. The influence of river discharge and wind speed on salinity diffusion was then investigated. The simulation results showed that under the action of residual currents, fresh water from the Yellow River spread to Laizhou Bay, and the low salinity area of Laizhou Bay was mainly distributed in the northwest. The envelope area of 27 psu isohaline can account for about one-quarter of Laizhou Bay in the wet season, while the low-salinity area was only concentrated near the estuary of Yellow River in the dry season. River discharge mainly affects the diffusion area and depth of fresh water, and wind can change the diffusion structure and direction. In the wet season, with the increase in wind speed, the surface area of the plume decreased gradually, and the direction of the fresh water plume changed counterclockwise from south to north. During the dry season, the plume spread to the northwest along the nearshore. The increase in wind speed in the early stage increased the surface plume area, and the plume area decreased above a wind speed of 10 m/s due to the change in the turbulence structure. The model developed and the results from this study provide valuable information for establishing robust water resource regulations for the Yellow River. This is particularly important to ensure that the areas with low salinity in the Yellow River Estuary will not decrease and affect the reproduction of fish species

A label free electrochemical nanobiosensor study

Nano-porous silicon (PS) is an attractive material for incorporation into biosensors, because it has a large surface area combined with the ability to generate both optical and electrical signals. In this paper, we describe a label-free nanobiosensor for bovine serum albumin (BSA). Nano-porous silicon produced in our laboratory was functionalized prior to immobilization of anti-BSA antibody on the surface. Reaction with BSA in phosphate buffered saline (PBS) buffer resulted in an impedance change which was inversely proportional to the concentration of the analyte. The system PBS buffer/antigen-antibody/PS constitutes an electrolyte-insulator-semiconductor (EIS) structure, thus furnishing an impedance EIS nanobiosensor. The linear range of the sensor was 0-0.27 mg mL-1 and the sensitivity was less than 10 µg mL-1

Association between Serum Ferritin and Contrast-Induced Nephropathy in Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

Background and Aims. CIN is a major and serious complication following PCI in patients with ACS. It is unclear whether a higher serum ferritin level is associated with an increased risk of CIN in high-risk patients. Thus, we conducted this study to assess the predictive value of SF for the risk of CIN after PCI. Methods. We prospectively examined SF levels in 548 patients with ACS before undergoing PCI. Multivariate logistic regression analysis was used to analyze the independent risk factors for CIN. The ROC analysis was performed to evaluate the predictive value of SF for CIN. Results. CIN occurred in 96 patients. Baseline SF was higher in patients who developed CIN compared to those who did not (257.05±93.98 versus 211.67±106.65; P<0.001). Multivariate logistic regression analysis showed that SF was an independent predictor of CIN (OR, 1.008; 95% CI, 1.003–1.013; P=0.002). The area under ROC curve for SF was 0.629, and SF > 180.9 μg/L predicted CIN with sensitivity of 80.2% and specificity of 41.4%. Conclusion. Our data show that a higher SF level was significantly associated with an increased risk of CIN after PCI