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Intensive aquaculture in high-density hybrid snakehead [Channa maculata (♀) × Channa argus (♂)] fishponds can lead to toxic conditions for fish. This study investigated nitrogen migration and transformation in these fishponds during different cultivation periods. Using qPCR technology, we analyzed the abundance variation of nitrogen-cycling microorganisms in water and sediment to reveal the nitrogen metabolism characteristics of hybrid snakehead fishponds. The results showed that fish biomass significantly impacts suspended particulate matter (SPM) flux. At the sediment–water interface, inorganic nitrogen fluxes showed predominant NO3−-N absorption by sediments and NH4+-N and NO2−-N release, especially in later cultivation stages. Sediments were rich in nirS and AMX 16S rRNA genes (ranging from 4.04 × 109 to 1.01 × 1010 and 1.19 × 108 to 2.62 × 108 copies/g, respectively) with nirS-type denitrifiers potentially dominating the denitrification process. Ammonia-oxidizing bacteria (AOB) were found to dominate the ammonia oxidation process over ammonia-oxidizing archaea (AOA) in both water and sediment. Redundancy analysis revealed a positive correlation between SPM flux, Chlorophyll a (Chl-a), and denitrification genes in the water, and between nitrogen-cycling genes and NH4+/NO2− fluxes at the interface. These findings provide a scientific basis for nitrogen control in hybrid snakehead fishponds.

Soil microorganism and their relationships with soil respiration in paddy systems in karst areas (KA) of southern China is important for understanding the mechanisms of greenhouse gas emission reduction. Soils were collected from the tillage layer (0–20 cm) during the rice growing season from KA and non-karst areas (NKA) (red soils) from the Guilin Karst Experimental Site in China. Community structures and inferred functionalities of bacteria and fungi were analyzed using the high-throughput sequencing techniques, FAPROTAX and FUNGuild. A bacterial–fungal co-occurrence network was constructed and soil respiration was measured using dark box-gas chromatography and built their relationships. The results indicated that soil respiration was significantly lower in KA than in NKA. Principal component analysis indicated that bacterial and fungal community structures significantly differed between KA and NKA. The OTU ratio of fungi to bacteria (F/B) was positively correlated with soil respiration (p = 0.044). Further, the key network microorganisms were OTU69 and OTU1133 and OTU1599 in the KA. Soil respiration negatively correlated with Acidobacteria Gp6, dung saprotroph-endophyte-litter saprotroph-undefined saprotroph, aerobic nitrite oxidizers and nitrifier in KA (p < 0.05). Overall, this study demonstrated that soil respiration was reduced when soil microorganisms shifted from bacterial to fungal dominance during the rice growing season in KA.

We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete Fourier transform (DFT) to extract fifteen features from vibration signals in the time and frequency domains of four bearing failure forms, which addresses the issue of ambiguous fault identification caused by their nonlinearity and nonstationarity. The extracted feature vectors are then divided into training and test sets as SVM inputs for fault diagnosis. To optimize the SVM, we construct a hybrid kernel SVM using a polynomial kernel function and radial basis kernel function. BO is used to optimize the extreme values of the objective function and determine their weight coefficients. We create an objective function for the Gaussian regression process of BO using training and test data as inputs, respectively. The optimized parameters are used to rebuild the SVM, which is then trained for network classification prediction. We tested the proposed diagnostic model using the bearing dataset of the Case Western Reserve University. The verification results show that the fault diagnosis accuracy is improved from 85% to 100% compared with the direct input of vibration signal into the SVM, and the effect is significant. Compared with other diagnostic models, our Bayesian-optimized hybrid kernel SVM model has the highest accuracy. In laboratory verification, we took sixty sets of sample values for each of the four failure forms measured in the experiment, and the verification process was repeated. The experimental results showed that the accuracy of the Bayesian-optimized hybrid kernel SVM reached 100%, and the accuracy of five replicates reached 96.7%. These results demonstrate the feasibility and superiority of our proposed method for fault diagnosis in rolling bearings.

This work introduces a one-step method for the preparation of layered oxide cathode materials utilizing pure Ni and Co mixed solution obtained from the waste hydrodesulfurization (HDS) catalyst. An efficient non-separation strategy with pyrometallurgical-hydrometallurgical (pyro-hydrometallurgical) process consisting of roasting and leaching is proposed. Most of the impurity metal elements such as Mo and V were removed by simple water leaching after the waste HDS catalyst was roasted with Na2CO3 at 650 °C for 2.5 h. Additionally, 93.9% Ni and 100.0% Co were recovered by H2SO4 leaching at 90 °C for 2.5 h. Then, LiNi0.533Co0.193Mn0.260V0.003Fe0.007Al0.004O2 (C–NCM) was successfully synthesized by hydroxide co-precipitation and high temperature solid phase methods using the above Ni and Co mixed solution. The final C–NCM material exhibits excellent electrochemical performance with a discharge specific capacity of 199.1 mAh g−1 at 0.1 C and a cycle retention rate of 79.7% after 200 cycles at 1 C. This novel process for the synthesis of cathode material can significantly improve production efficiency and realize the high added-value utilization of metal resources in a waste catalyst.

With the continuous development of hyperspectral image technology and deep learning methods in recent years, an increasing number of hyperspectral image classification models have been proposed. However, due to the numerous spectral dimensions of hyperspectral images, most classification models suffer from issues such as breaking spectral continuity and poor learning of spectral information. In this paper, we propose a new classification model called the enhanced spectral fusion network (ESFNet), which contains two parts: an optimized multi-scale fused spectral attention module (FsSE) and a 3D convolutional neural network (3D CNN) based on the fusion of different spectral strides (SSFCNN). Specifically, after sampling the hyperspectral images, our model first implements the weighting of the spectral information through the FsSE module to obtain spectral data with a higher degree of information richness. Then, the weighted spectral data are fed into the SSFCNN to realize the effective learning of spectral features. The new model can maximize the retention of spectral continuity and enhance the spectral information while being able to better utilize the enhanced information to improve the model’s ability to learn hyperspectral image features, thus improving the classification accuracy of the model. Experiment results on the Indian Pines and Pavia University datasets demonstrated that our method outperforms other relevant baselines in terms of classification accuracy and generalization performance.

A rolling bearing fault diagnosis method based on whale gray wolf optimization algorithm-variational mode decomposition-support vector machine (WGWOA-VMD-SVM) was proposed to solve the unclear fault characterization of rolling bearing vibration signal due to its nonlinear and nonstationary characteristics. A whale gray wolf optimization algorithm (WGWOA) was proposed by combining whale optimization algorithm (WOA) and gray wolf optimization (GWO), and the rolling bearing signal was decomposed by using variational mode decomposition (VMD). Each eigenvalue was extracted as eigenvector after VMD, and the training and test sets of the fault diagnosis model were divided accordingly. The support vector machine (SVM) was used as the fault diagnosis model and optimized by using WGWOA. The validity of this method was verified by two cases of Case Western Reserve University bearing data set and laboratory test. The test results show that in the bearing data set of Case Western Reserve University, compared with the existing VMD-SVM method, the fault diagnosis accuracy rate of the WGWOA-VMD-SVM method in five repeated tests reaches 100.00%, which preliminarily verifies the feasibility of this algorithm. In the laboratory test case, the diagnostic effect of the proposed fault diagnosis method is compared with backpropagation neural network, SVM, VMD-SVM, WOA-VMD-SVM, GWO-VMD-SVM, and WGWOA-VMD-SVM. Test results show that the accuracy rate of WGWOA-VMD-SVM fault diagnosis is the highest, the accuracy rate of a single test reaches 100.00%, and the accuracy rate of five repeated tests reaches 99.75%, which is the highest compared with the above six methods. WGWOA plays a good optimization role in optimizing VMD and SVM. The signal decomposed by VMD is optimized by using the WGWOA algorithm without mode overlap. WGWOA has the better convergence performance than WOA and GWO, which further verifies its superiority among the compared methods. The research results can provide an effective improvement method for the existing rolling bearing fault diagnosis technology.

MoS2/C nanocomposite coatings were deposited on a 304 stainless steel plate by unbalanced magnetron sputtering from carbon and molybdenum disulfide targets, and the target current of MoS2 was varied to prepare for coating with different carbon contents. The mechanical and tribological properties of the MoS2/C nanocomposite coating with different carbon contents were studied using a low-velocity impact wear machine based on kinetic energy control, and the substrate was used as the comparison material. The atomic content ratio of Mo to S in the MoS2/C coating prepared by unbalanced magnetron sputtering was approximately 1.3. The dynamic response and damage analysis revealed that the coating exhibited good impact wear resistance. Under the same experimental conditions, the wear depth of the MoS2/C coating was lower than that of the substrate, and the coating exhibited a different dynamic response process as the carbon content increased.