Displacement prediction of tunnel entrance slope based on LSSVM and bacterial foraging optimization algorithm.

In order to realize the effective prediction of landslide risk in the tunnel entrance area, an multivariate time series model is established on the basis of the traditional model, taking temperature and rainfall factors as additional input indicators. Bacterial foraging optimization algorithm (BFOA) is used to search the global optimal solution of the key parameters γ and [Formula: see text] of least squares support vector machine (LSSVM) to improve its regression accuracy, and the evolved LSSVM is used to describe the aforementioned multivariate time series model. At the same time, a remote real-time internet of things (IoT) monitoring system for the tunnel entrance section, including monitoring indicators such as surface subsidence, temperature, and rainfall, has also been designed and implemented, providing a stable and accurate data source for the realization of this prediction model.

[Soil denitrification rates in different type riparian zones in maoershan mountainous region of China].

Soil samples were collected from forested, clear-cut, and grassy riparian zones under forest background and from forested and barren riparian zones under cropland background in the Maoershan mountainous region of China. The samples were incubated in laboratory, and their denitrification potentials were determined by nitrate-deduction method. The results showed that under crop-land background, soil denitrification rate was the highest in forested riparian zone and the lowest in barren riparian zone, with the deduction rate of nitrate varied from 46.

Ultrasound-assisted one-pot approach to alpha-amino phosphonates under solvent-free and catalyst-free conditions.

alpha-Amino phosphonates could be obtained in good to excellent yields by the three-component coupling of aldehydes, amines and diethylphosphite in one-pot procedure under ultrasound-assisted solvent-free and catalyst-free conditions.