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Water-level fluctuation (WLF) forecast plays an essential role in the regulations and management of the Yangtze River’s navigable rivers. Traditional methods for navigable rivers’ forecasts are often time-consuming and cost-inefficient, often requiring constant updates and are univariate in nature. This study utilized one of the Yangtze River’s navigable rivers, Nanjing navigable river, to perform forecasts employing deep learning techniques. For the first time, Nanjing navigable river’s WLF data was exploited to make multi-step univariate and multivariate time series forecasts, employing a deep learning technique, multilayer perceptron (MLP). The data consisted of 3545 days worth of WLF measurements from ten different navigable rivers and was allocated properly without data leakage into training and testing. MLP was built to take time series sequence inputs from up to ten navigable rivers on a daily basis and output fast, accurate, stable, and reliable short-term forecasts and undeviating trends for long-term forecasts. The models were also constructed in an adaptive way so they can self-update daily based on the daily measurements. The naive model was constructed as a baseline model to measure the improvement and the validity of MLP models. Univariate and multivariate adaptive models were then constructed based on the augmented data by a popular data-augmentation method, rolling windows. Hyperparameters of MLP models were optimized based on relevant testing and large scale grid-search. Both short-term and long-term forecasts showed promises of MLP model in time series forecast for navigable river water-level problems and they achieved at least 27.6 % percent lower in root mean square error (RMSE) compared to the baseline. Since the total time required for the trained models to produce forecasts and self-update is at most 15 min on a local quad-core Intel Core i5 10th generation (2.0 GHz) and because of the nonnegotiable performance of the models, it was concluded that MLP is a fast, cost-efficient, and accurate alternative technique for navigable rivers WLF forecasts.

Accumulation of single nucleotide polymorphisms (SNPs) in the displacement loop (D-loop) of mitochondrial DNA (mtDNA) has been identified to be associated with cancer risk and disease outcome. In this study, we investigated whether the SNPs in mitochondrial D-loop were associated with the outcome of malignant melanoma (MM) in Chinese Han population.The D-loop region of mtDNA was sequenced for 75 MM patients recorded in the Fourth Hospital of Hebei Medical University between 2003 and 2009. The 5-year survival curve were calculated with the Kaplan-Meier method and compared by the log-rank test at each SNP site, a multivariate survival analysis was also performed with the Cox proportional hazards method.The SNP sites of nucleotides T204C, A235G and T16519C were identified for prediction of post-operational survival by the log-rank test. In an overall multivariate analysis, the T204C and A235G alleles were identified as independent predictors of MM outcome.Genetic polymorphisms in the D-loop are independent prognostic markers for patients with MM. Accordingly, the analysis of genetic polymorphisms in the mitochondrial D-loop can help identify patient subgroups at high risk of a poor disease outcome.

In this study, the titanium plates, which were modified by NaOH alkali solution, were associated with 3-Aminopropyltriethoxysilane (APTES) films using self-assembled monolayers (SAMs). The surfaces of titanium before and after modification were characterized by scanning electronic microscopy (SEM), X-ray photoelectron spectroscopy (XPS) and attenuated total refraction–Fourier transform infrared spectroscopy (ATR–FTIR). After bonding the APTES films on the modification titanium, the new peaks located around 1095 cm −1 attributes to siloxane groups indicating that silane agent had been grafted onto the surface of the modification titanium substrate by SAMs. Following the deposition of APTES films on titanium, significant change were seen in the amounts of oxygen, silicon and carbon present on the titanium surface, which were consistent with the anticipated reaction steps.