In this study, the authors propose a method that combines CNN and LSTM networks to recognize facial expressions. To handle illumination changes and preserve edge information in the image, the method uses two different preprocessing techniques. The preprocessed image is then fed into two independent CNN layers for feature extraction. The extracted features are then fused with an LSTM layer to capture the temporal dynamics of facial expressions. To evaluate the method's performance, the authors use the FER2013 dataset, which contains over 35,000 facial images with seven different expressions. To ensure a balanced distribution of the expressions in the training and testing sets, a mixing matrix is generated. The models in FER on the FER2013 dataset with an accuracy of 73.72%. The use of Focal loss, a variant of cross-entropy loss, improves the model's performance, especially in handling class imbalance. Overall, the proposed method demonstrates strong generalization ability and robustness to variations in illumination and facial expressions. It has the potential to be applied in various real-world applications such as emotion recognition in virtual assistants, driver monitoring systems, and mental health diagnosis.

With the advent of the era of globalized economy, more attention should be paid to the training mode of comprehensive English literacy in colleges and universities in teaching. Therefore, how to cultivate and improve students' higher-level teaching methods in the context of ecolinguistics, so as to improve the quality of English teaching, is the current focus of English teaching in colleges and universities. By summarizing the basic concepts of ecolinguistics, this paper studies effective measures to improve the quality of linguistics teaching in colleges and universities, and comprehensively improves the quality and efficiency of English language teaching in colleges and universities under the background of ecolinguistics.

Lattice Boltzmann models for diffusion equation are generally in Cartesian coordinate system. Very few researchers have attempted to solve diffusion equation in spherical coordinate system. In the lattice Boltzmann based diffusion model in spherical coordinate system extra term, which is due to variation of surface area along radial direction, is modeled as source term. In this study diffusion equation in spherical coordinate system is first converted to diffusion equation which is similar to that in Cartesian coordinate system by using proper variable. The diffusion equation is then solved using standard lattice Boltzmann method. The results obtained for the new variable are again converted to the actual variable. The numerical scheme is verified by comparing the results of the simulation study with analytical solution. A good agreement between the two results is established.

Fe3+-doped nano-TiO2 powders were prepared by sol-gel method. The photocatalytic activity of Fe3+-doped TiO2 nanoparticles was studied by using UV lamp as light source and methylene blue as degradation target. The photocatalytic activity of Fe3+-doped TiO2 was studied by degradation of 4L methylene blue solution with initial concentration of 10mg · L - 1. The results show that the photocatalytic activity of TiO2 can be improved by the addition of Fe3+. When the molar ratio of Fe3+ is 0.5-1%, the calcination temperature is 500 ℃. The photocatalytic degradation of methylene blue is the best.