Study on the mechanism of nitrapyrin microcapsule suspension effectively improving nitrification inhibition rate in black soil.

Nitrification inhibitors (NIs) have been widely applied to inhibit nitrification and reduce N2O emissions in agriculture. However, there are still some shortcomings, e.g. short effective periods, large applying amounts, low effectiveness, easy deactivation and different effect. Thus, a nitrapyrin microcapsule suspension (CPCS) was used as a new experimental material to elaborate its effects on nitrogen transformation and microbial response mechanisms in black soil by cultivation experiments with six treatments of no fertilization (CK), urea, urea+ 0.2 % CPES, urea+ 0.1 % CPCS, urea+ 0.2 % CPCS, and urea+ 0.3 % CPCS. The content of ammonium, nitrate nitrogen, functional microbial activity, degradation rate and adsorption characteristics of CPCS in the soil at different incubating times were determine. Compared with the nitrapyrin emulsifiable concentrate (CPEC) treatment, the degradation rate of CPCS decreased by 21.54 %, the half-life increased by 10.2 days, and the adsorption rate of nitrapyrin on black soil decreased more than 6-fold. CPCS effectively inhibited the transformation of ammonium nitrogen to nitrate nitrogen within more than 42 days. CPCS had a negative effect on amoA gene abundance and a positive effect on nrfA gene abundance. The research results provide a basic theoretical support for the application of CPCS on black soil.

A study on and adsorption mechanism of ammonium nitrogen by modified corn straw biochar.

Using corn stover as raw material, the adsorption mechanism of ammonium nitrogen by biochar prepared by different modification methods was studied. The biochar was characterized by Fourier transform infrared spectroscopy, surface-area analysis and scanning electron microscopy. The results showed that the adsorption of NH 4 + - N by different modified biochars confirmed the quasi-second-order kinetic equation (R 2 > 0.95, p ≤ 0.05), the adsorption isotherms of the Langmuir equation (R 2 ≥ 0.96, p ≤ 0.05). ΔG θ < 0, ΔH θ > 0 indicated that the adsorption of NH 4 + - N by different modified biochars was a spontaneous endothermic reaction. With the increase in adsorption temperature, the adsorption capacity of biochar to ammonium nitrogen increased gradually. The adsorption was monolayer adsorption and was controlled by a fast reaction. Both KOH and FeCl3 modified biochars significantly improved the adsorption capacity of NH 4 + - N , and the adsorption mechanism was different. The adsorption capacity of NH 4 + - N by FeCl3 modified biochars mainly increased the specific surface area and micropore volume. The adsorption of ammonium nitrogen after KOH modification primarily depended on the wealthy oxygen-containing functional groups. The adsorption effect of ammonium nitrogen modified by KOH was better than that of biochar modified by FeCl3.

Pulmonary Nodule Detection Based on Multiscale Feature Fusion.

As cancer with the highest morbidity and mortality in the world, lung cancer is characterized by pulmonary nodules in the early stage. The detection of pulmonary nodules is an important method for the early detection of lung cancer, which can greatly improve the survival rate of lung cancer patients. However, the accuracy of conventional detection methods for lung nodules is low. With the development of medical imaging technology, deep learning plays an increasingly important role in medical image detection, and pulmonary nodules can be accurately detected by CT images. Based on the above, a pulmonary nodule detection method based on deep learning is proposed. In the candidate nodule detection stage, the multiscale features and Faster R-CNN, a general-purpose detection framework based on deep learning, were combined together to improve the detection of small-sized lung nodules. In the false-positive nodule filtration stage, a 3D convolutional neural network based on multiscale fusion is designed to reduce false-positive nodules. The experiment results show that the candidate nodule detection model based on Faster R-CNN integrating multiscale features has achieved a sensitivity of 98.6%, 10% higher than that of the other single-scale model, the proposed method achieved a sensitivity of 90.5% at the level of 4 false-positive nodules per scan, and the CPM score reached 0.829. The results are higher than methods in other works of literature. It can be seen that the detection method of pulmonary nodules based on multiscale fusion has a higher detection rate for small nodules and improves the classification performance of true and false-positive pulmonary nodules. This will help doctors when making a lung cancer diagnosis.