Recognition of weather phenomena based on images is essential for the analysis of weather conditions. To address the problems that traditional machine learning methods are difficult to accurately extract various weather features and poor in classifying weather phenomena and the accuracy of deep learning for weather phenomena recognition is not high, a weather recognition model based on image block and multi?headed attention mechanism is proposed. The model introduces Swin Transformer into the field of weather recognition for the first time, and adopts a multi?headed attention mechanism combining window multi?head self?attention layer and shifted?window multi?head self?attention layer, whose regionally relevant features extraction capability makes up for the shortcomings of traditional methods and can extract complex weather features from images. The model is trained using transfer learning, and the fully connected parameters of the fine?tuned model are input to the Softmax classifier to achieve recognition of multi?category weather images with 99.20% recognition accuracy, which is better than several mainstream methods in comparison, and it can be applied to ground weather recognition systems as a weather recognition module.

Magnetic tomography method has been widely used for nondestructive external inspection of buried and submarine pipelines, which is based on the principle of metal magnetic memory to discern the danger level and location of the stress concentration zone by measuring the anomalies in the spatial magnetic field distribution outside the pipeline. The distribution characteristics and spatial propagation law of pipeline inspection signal detected by magnetic tomography method, the energy distribution and change law of spatial magnetic memory signal in the stress concentration zone of magnetized pipelines are studied in this paper. The magnetic dipole field is used to establish the magnetic field model in the stress concentration zone of the inner wall of the pipeline, and the magnetic energy and energy density of spatial magnetic memory signals under different lift?off values outside the pipeline are finite element calculated based on the magnetic energy theory to derive the distribution law of spatial magnetic field and the correlation of magnetic energy density of magnetic signals under different lift?off value is analyzed. The results show that the spatial magnetic field energy outside the pipe decays with the increase of lift?off value, and the decay is the fastest within the distance of 50 mm from the outer wall of the pipe to the physical force; the correlation of magnetic energy density of different lift?off values shows that the magnetic signal detected by magnetic tomography method outside the pipe is homologous with the signal in the stress concentration zone of the inner wall of the pipe. Theoretically, it explains the effectiveness of magnetic tomography method and also provides evaluation indexes for extracting effective signals from the detection data.

The COVID?19 epidemic is facing the influence of a variety of complex practical factors, which makes the development of the epidemic uncertain. In order to overcome the problem of large error in epidemic forecasting results due to the limitations of many ideal assumptions based on the infectious disease compartment model, a time series forecasting model based on deep learning is adopted to predict the epidemic development, and an informer model based on transformer model is established. Attention mechanism and distillation mechanism are applied to the time series forecasting of epidemic data. The threshold autoregressive (TAR) model and a variety of mainstream recurrent neural time series prediction models are used as comparison models. Through simulation experiments, the current number of remaining infections in the epidemic data of China, America and Britain is predicted in the short term, and RMSE and MAE are used as evaluation indicators, and then the best model is selected for medium ? and long?term prediction. The experimental results show that the indicator value of the informer model is optimal in both RMSE and MAE, further indicating that the prediction accuracy of the informer model is higher than that of other comparative models in China, America and Britain. Finally, the Informer model is used for the development of the epidemic in China，America and Britain medium and long?term prediction.

Mergers and acquisitions is an effective way to rapidly expand the scale of enterprises and achieve profit growth. This article selected the M&A events of China's media industry listed companies from 2016 to 2017 as a sample, and conducted an empirical analysis on the relationship between CEO tenure, internal control, and media industry M&A performance. The results show that the M&A performance of the media industry has a tendency to rise first and then decline after the M&A; CEO tenure is negatively correlated with the M&A performance of the media industry; under the premise that other conditions remain unchanged, the effectiveness of media industry corporate internal control will promote the improvement of M&A performance; perfect internal control can weaken the negative correlation between CEO tenure and M&A performance in media industry. Based on the above research conclusions, some suggestions were put forward to improve the M&A performance of enterprises in the media industry.

In order to reduce the heat resistance and improve stability of convective heat transfer in liquefied natural gas(LNG) vaporizer, the flow and heat transfer processes of supercritical pressure LNG were analyzed by numerical method in a vertical tube. The influence of heat fluxes and flow directions on heat transfer characteristics was discussed, and the effects of the flow field , the temperature field , and the turbulent kinetic energy changes on the heat transfer instability were investigated.The results show that the heat transfer of supercritical methane in vertical tube is unstable,at high heat flux density, the inner wall temperature and the average temperature of the pipe are unstable and and shock in the heat transfer deterioration range; At the low heat flow density , the local convection heat transfer coefficient is unstable , and it shocks in the heat transfer deterioration range; Under the same heat flux density, the heat transfer instability of upward flow is greater than that of downward flow, because the thermal influence of upward flow is greater than that of downward flow. The gas?like film is the main reason of heat transfer instability.

Titanium sulfate was used as a raw material, and a titanium oxide (TiO₂) was prepared by direct high?temperature calcination in a muffle furnace. FT?IR, XRD, UV?Vis, SEM were used to characterize the structure of the catalyst. The results show that the direct calcination method can prepare anatase titanium dioxide and apply it to the oxidative desulfurization of dibenzothiophene. Using acetonitrile as the extractant and titanium oxide as the catalyst, the oxidation method was used to remove dibenzothiophene from the simulated oil. The effects of catalyst dosage, reaction temperature, n(H₂O₂)/n(S), and different sulfur compounds on the desulfurization effect were investigated, and the recycling performance of the catalyst was also investigated. Under the optimal desulfurization conditions, the desulfurization rates of dibenzothiophene, 4,6?dimethyldibenzothiophene, benzothiophene, and mixed diesel are 99.5%, 35.6%, 65.0%, and 53.4%. After the catalyst was recycled five times, the catalytic desulfurization effect was still as high as 90.3%.

The immersion and electrochemical methods has been used to study the corrosion behavior of automotive steel Q255 in different content of snow melting salt (NaCl) solution. Immersion test show that, the snow melting salt solution is more corrosive than the natural snow solution. The corrosion rate of Q255 steel in the natural water water is smaller than that of the snow melting salt solution, with the corrosion rate of 0.021 07 mm/a after the immersion of 168 h.The corrosion rate increased with the increase of NaCl concentration of the snow solution. The corrosion rate of Q255 steel could reach 0.039 71 mm/a in 3%NaCl solution, the value of which was almost double to the natural snow solution. Electrochemical experiments show that, the presence of chloride ions accelerated the dissolution and mass transfer process of F e 2+ corrosion products, subsequently reduced the adhesion of the corrosion product. Accordingly, the corrosion products would not be able to protect the steel surface, thus the corrosion rate accelerated.