Multi-frequency PolSAR Image Fusion Classification Based on Semantic Interactive Information and Topological Structure

Compared with the rapid development of single-frequency multi-polarization SAR image classification technology, there is less research on the land cover classification of multifrequency polarimetric SAR (MF-PolSAR) images. In addition, the current deep learning methods for MF-PolSAR classification are mainly based on convolutional neural networks (CNNs), only local spatiality is considered but the nonlocal relationship is ignored. Therefore, based on semantic interaction and nonlocal topological structure, this paper proposes the MF semantics and topology fusion network (MF-STFnet) to improve MF-PolSAR classification performance. In MF-STFnet, two kinds of classification are implemented for each band, semantic information-based (SIC) and topological property-based (TPC). They work collaboratively during MF-STFnet training, which can not only fully leverage the complementarity of bands, but also combine local and nonlocal spatial information to improve the discrimination between different categories. For SIC, the designed crossband interactive feature extraction module (CIFEM) is embedded to explicitly model the deep semantic correlation among bands, thereby leveraging the complementarity of bands to make ground objects more separable. For TPC, the graph sample and aggregate network (GraphSAGE) is employed to dynamically capture the representation of nonlocal topological relations between land cover categories. In this way, the robustness of classification can be further improved by combining nonlocal spatial information. Finally, an adaptive weighting fusion (AWF) strategy is proposed to merge inference from different bands, so as to make the MF joint classification decisions of SIC and TPC. The comparative experiments show that MF-STFnet can achieve more competitive classification performance than some state-of-the-art methods

Start-Up Mechanism and Dynamic Process of Landslides in the Full High Waste Dump

Landslides often occur in the open-pit mine dump, which is harmful to the safety operation of mines and slopes. In this work, the landslides that occurred in 2014 at Nanfen open-pit mine of China are studied to understand the triggering mechanism and dynamic process of landslides in the full high waste dump. Field investigation, hydrogeological data analysis, satellite map data, and numerical simulation are combined to analyze and evaluate the landslides. The study shows that the continuous and intensive dumping can lead to shear failure under the action of self-weight. The shear strength of loose dump bodies significantly relies on the water content, freeze-thaw cycle, pore pressure, and gradation of the dump soils. These factors result in the occurrence of landslides in the dump slope. The predictions by the smoothed particle hydrodynamics method show that the shape, influence range, and slip distance of landslides are consistent with that of the field investigation. The present study shows that the SPH method is a powerful numerical technique to describe landslides’ problems

3D Numerical Simulation of Landslides for the Full High Waste Dump Using SPH Method

Waste dump that is generally composed of a large number of loose geotechnical materials is prone to landslides under external loads. In this work, the smoothed particle hydrodynamics (SPH) method combined with the Mohr–Coulomb model is used to study the dynamic characteristics of the landslides that occurred in the waste dump during the failure process. A benchmark test is firstly conducted to verify the effectiveness of the SPH model. Then, taking the Nanfen full high waste dump with a vertical drop of 300 m in Benxi City, China, as an example, the most dangerous section is selected to establish the SPH numerical model for the waste dump landslides, and the overall dynamic process of the landslides is simulated. The simulation results show that the particles in the middle and upper of the slope have larger potential energy, and their sliding distance is larger. On the contrary, the sliding distance of particles in the lower of the slope is smaller. The particles' sliding distance decreases as the depth increases in the vertical direction of both shoulder and middle of the slope. The particles undergo a process of first acceleration and then deceleration. The sliding distance is in good agreement with the field survey result, and the landslides profile is basically consistent with the actual one. The sensitivity analysis of different particle numbers shows that the number of particles has little effect on the numerical results. The SPH method can vividly reproduce the dynamic process of the landslides in the full high waste dump. The evaluation of the sliding characteristics and risk impact range can provide the key parameters and basis for the prevention and control of the landslides in the full high waste dump and ensure the safety of the mine life cycle

Synthesis of highly pure single crystalline SnSe nanostructures by thermal evaporation and condensation route

Here we report the synthesis of highly pure single crystalline tin selenide (SnSe) nanospheres by pretreatment of precursors with aqueous ammonia. In this work we have demonstrated that aqueous ammonia not only controls the preferred growth orientation but also controls the morphology of SnSe. Chemical vapor deposition technique was used for the growth of SnSe nanostructures. The optical properties were studied using UV-vis-NIR spectroscopy and Photoluminescence (PL) spectrum

Comparison of ACE inhibitory activity in skimmed goat and cow milk hydrolyzed by alcalase, flavourzyme, neutral protease and proteinase K

Angiotensin I converting enzyme (ACE) inhibitory peptides derived from milk proteins have obvious effect of lowering blood pressure, safe and non-toxic side effects. This study compared four commercial proteases, namely alcalase, flavourzyme, neutral protease and proteinase K for their ACE inhibitory activity in skimmed goat and cow milk and identified the best one with higher ACE inhibitory activity. The degree of hydrolysis (DH) of alcalase and proteinase K were much higher than flavourzyme, neutral protease for both skimmed goat and cow milk. Alcalase was the best enzyme to produce ACE inhibitory peptides from goat milk, with the ACE inhibitory activity 95.31%, while proteinase K was the optimal protease for hydrolyzing cow milk, with 81.28% ACE inhibitory activity. Furthermore, no correlation was obtained between the ACE inhibitory activity and DH for both goat and cow milk

Optimization of Fermentation Conditions for the Production of Angiotensin-Converting Enzyme (ACE) Inhibitory Peptides from Cow Milk by Lactobacillus bulgaricus LB6

The purpose of this research was to screen out the optimal -producing peptide conditions for cow milk fermented by Lactobacillus bulgaricus LB6. The effects of temperature, inoculation size, time and skim milk concentration on the ACE inhibition rate of fermented milk were investigated by single factor experiment, and the optimal fermentation conditions were determined by orthogonal experiment. The conditions of the single factor experiment were: Temperatures were 37° C, 39° C, 42° C, 44° C and 46° C. The inoculation amount was 1%, 3%, 5%, 7% and 9%, the time was 8h and 10h. At 12h, 14h and 16h, the concentration of skim milk was 8%, 10%, 12%, 14% and 16%, respectively. The results showed that the optimal fermentation conditions for ACE inhibitory peptide produced by Lactobacillus bulgaricus LB6 were 4% inoculation, 13h in time, 42°C in temperature and 13% in skim milk. Under this condition, the ACE inhibition rate reached 76.50% and the OD value was 0.330. The titration acidity was 116.4°T, the pH was 4.62, and the sensory evaluation was 75 scores

Optimization of Emulsifying Effectiveness of Phytosterol in Milk Using Two-Level Fractional Factorial Design

In this paper emulsifying effects of seven emulsifiers including Tween 80, Span 80, tripolyglycerol monostearate, sodium stearoyl lactylate, sucrose ester, soy lecithin and monoglyceride on phytosterol in milk were investigated using single factor test and fractional factorial design. The addition for seven emulsifiers were in the following concentrations: 0.1%, 0.2%, 0.3%, 0.4%, 0.5% and 0.6%. The results revealed that tripolyglycerol monostearate, sucrose ester and monoglyceride had a significant emulsifying effect on phytosterol in milk, Tripolyglycerol monostearate showed a positive emulsifying effect on phytosterol in milk, while sucrose ester and monoglyceride exhibited a negative emulsifying effect on phytosterol in milk

Optimization of Culture Medium for Lactobacillus bulgaricus using Box-Behnken Design

Lactobacillus bulgaricus is a common yogurt starter in dairy production. But the viable counts of the bacteria in the productions are relatively low during free-drying and storage which is not good for its commercial production. In order to obtain a medium with high activity and high density for bacterial cultured, the experiments and regression analysis were conducted by Box-Behnken design in this study, and a model was established to predict the influence of glucose (9-11 g·L−1), casein hydrolysate (15-17 g·L−1) and glutamate (6.5-7.5 mg·L−1) on viable counts of L. bulgaricus and. The results showed that the glucose, 9.5 g·L−1; casein hydrolysate, 15.5 g·L−1; glutamate, 7.0mg·L−1, the number of viable bacteria of L. bulgaricus could reach (2.95±0.07) ×109, which was very similar to the predicted value of the model of 3.00×109 cfu·mL−1, indicating that the optimized conditions and models used were feasible and effective. The optimized medium components can improve the viable counts of bacteria which are useful from its application in industrial production