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As a new concept of engineering education around the world, STEM (Science, Technology, Engineering, and Mathematics) education has become the focus in academic circles. However, there are many realistic puzzles on how to realize the Chinese teaching of STEM education. This study proposes a new design of teaching model, namely the Problem-Based Learning (PBL) teaching model based on the concept of STEM education (hereinafter referred to as the PBLbSTEM), that is, the design of talent training objectives focusing on technological innovation literacy, engineering humanities literacy and data intelligence literacy; the design of scientific programming focusing on inquiry -practice -communication; the design of multi-dimensional evaluation based on evidence-based practice; and the design of environmental protection promoting the integration of industry and academia, as well as professional and general education. After three rounds of teaching practice in the Discrete Mathematics course, it is proved that this model can improve students’ learning engagement, enhance students’ above three literacy, enhance students’ learning experience and teaching satisfaction, and promote the deep integration of industry and academic research, which can provide reference for the teaching reform of first-class undergraduate education and the cultivation of innovative talents of engineering science and technology in colleges and universities.

In order to obtain the pattern of variation of PM 2.5 concentrations in the atmosphere in Nanchang City, we build a Support Vector Regression(SVR) with modified Whale Optimization Algorithm(WOA) hybrid model (namely mWOA-SVR model) that can predict the PM 2.5 concentration. Firstly, according to the Pearson correlation coefficient (PCC) method to examine the dynamic relationship between air pollutants and meteorological factors together with them, PM 10 , SO 2 and CO were selected as air pollutant concentration characteristics, while daily maximum and minimum temperatures, and wind power levels were selected as meteorological characteristics; then, using modified WOA algorithm for parameter selection of SVR model, four sets of better parameter combinations were found; finally, the mWOA-SVR model was built by the four sets parameters to predict PM 2.5 concentration. The results show that the prediction accuracy of mixed mWOA-SVR model with pollutant concentration plus weather factors as the feature was higher than single pollutant concentration.

With the rapid expansion of industrialization and urbanization, fine Particulate Matter (PM 2.5 ) pollution has escalated into a major global environmental crisis. This pollution severely affects human health and ecosystem stability. Accurately predicting PM 2.5 levels is essential. However, air quality forecasting currently faces challenges in processing vast data and enhancing model accuracy. Deep learning models are widely applied for their superior learning and fitting abilities in haze prediction. Yet, they are limited by optimization challenges, long training periods, high data quality needs, and a tendency towards overfitting. Furthermore, the complex internal structures and mechanisms of these models complicate the understanding of haze formation. In contrast, traditional Support Vector Regression (SVR) methods perform well with complex non-linear data but struggle with increased data volumes. To address this, we developed CUDA-based code to optimize SVR algorithm efficiency. We also combined SVR with Genetic Algorithms (GA), Sparrow Search Algorithm (SSA), and Particle Swarm Optimization (PSO) to identify the optimal haze prediction model. Our results demonstrate that the model combining intelligent algorithms with Central Processing Unit-raphics Processing Unit (CPU-GPU) heterogeneous parallel computing significantly outpaces the PSO-SVR model in training speed. It achieves a computation time that is 6.21–35.34 times faster. Compared to other models, the Particle Swarm Optimization-Central Processing Unit-Graphics Processing Unit-Support Vector Regression (PSO-CPU-GPU-SVR) model stands out in haze prediction, offering substantial speed improvements and enhanced stability and reliability while maintaining high accuracy. This breakthrough not only advances the efficiency and accuracy of haze prediction but also provides valuable insights for real-time air quality monitoring and decision-making.

The concentration prediction of PM 2.5 plays a vital role in controlling the air and improving the environment. This paper proposes a prediction model (namely EEMD-ALSTM) based on Ensemble Empirical Mode Decomposition (EEMD), Attention Mechanism and Long Short-Term Memory network (LSTM). Through the combination of decomposition and LSTM, attention mechanism is introduced to realize the prediction of PM 2.5 concentration. The advantage of EEMD-ALSTM model is that it decomposes and combines the original data using the method of ensemble empirical mode decomposition, reduces the high nonlinearity of the original data, and Specially reintroduction the attention mechanism, which enhances the extraction and retention of data features by the model. Through experimental comparison, it was found that the EEMD-ALSTM model reduced its MAE and RMSE by about 15% while maintaining the same R 2 correlation coefficient, and the stability of the model in the prediction process was also improved significantly.

To mitigate the adverse effects of air pollution, accurate PM 2.5 prediction is particularly important. It is difficult for existing models to escape the limitations attached to a single model itself. This study proposes a hybrid PM 2.5 prediction model utilizing deep learning techniques, which aims to complement each other’s strengths through model fusion. The model integrates the transformer and LSTM architectures and employs parameter optimization through the particle swarm optimization (PSO) algorithm. The proposed model achieves superior performance by utilizing the gating mechanism of the LSTM model, the positional encoding and self-attention mechanism of the Transformer model, and PSO’s robust optimization capabilities. Experimental results show that the new model outperforms both the traditional LSTM model and the PSO-LSTM model in the PM 2.5 prediction task, and its evaluation metrics, R 2 , MAE, MBE, RMSE, and MAPE, are all improved. Furthermore, the model demonstrates stable performance across different cities and various periods. This study offers a robust approach to improving the accuracy and reliability of PM 2.5 forecasting.

To address the performance degradation in existing PM2.5 prediction models caused by excessive complexity, poor spatiotemporal efficiency, and suboptimal parameter optimization, we employ stacking ensemble learning for feature weighting analysis and integrate the ant colony optimization (ACO) algorithm for model parameter optimization. Combining meteorological and collaborative pollutant data, a model (namely the stacking-ACO-LSTM model) with a much shorter consuming time than that of only long short-term memory (LSTM) networks suitable for PM2.5 concentration prediction is established. It can effectively filter out feature variables with higher weights, thereby reducing the predictive power of the model. The prediction of hourly PM2.5 concentration of the model is trained and tested using real-time monitoring data in Nanchang City from 2017 to 2019. The results show that the established stacking-ACO-LSTM model has high accuracy in predicting PM2.5 concentration, and compared to the same model without considering time and space efficiency and defective parameter optimization, the mean square error (MSE) decreases by about 99.88%, and the coefficient of determination (R2) increases by about 2.39%. This study provides a new idea for predicting PM2.5 concentration in cities.

With the acceleration of urbanization in China, haze pollution has become a problem that cannot be ignored. PM 2.5 is one of the main components of haze, and this paper aims to find a stable and accurate prediction method for PM 2.5 prediction. Combined with existing studies, BP neural network is commonly used for prediction and optimization, but its accuracy is not satisfactory due to the randomness of the initial parameters of BP neural network. In order to solve this problem, this study proposes a new type of fusion model-improved particle swarm optimized backpropagation neural network (IPSO-BP) model. In this paper, we use the BP neural network to predict the value of PM 2.5 , and at the same time, we use the improved particle swarm algorithm to optimize the initial parameters of the BP neural network, which makes the prediction performance improved. Taking a simulation experiment in Nanchang City as an example, the prediction accuracy is 86.76%, the correlation coefficient R 2 is 0.95734, and the root-mean-square error (RMSE) is 5.2407. Compared with a single BP neural network model, the advantages of the IPSO-BP model are: (1) Asynchronous learning factor is used, particle swarm algorithm (PSO) exists two learning factors, individual learning factor c1 and population learning factor c2, the former affects the local search ability while the latter affects the global search ability. Through the iterative formula proposed in this paper, the algorithm can be made to satisfy the strong global search ability in the early stage and the strong local search ability in the later stage. (2) Adaptive inertia weights are introduced, where larger values of inertia weights mean that it is more difficult to change the direction of the particles. In the initial stage of the model, a larger inertia weight helps to improve the global search ability of the algorithm, while a smaller inertia weight helps to improve the local search ability of the algorithm as it enters the end of the search. Adaptive inertia weights are the iterative formulas proposed in the paper that make the inertia weights of the model large at the beginning and small at the end. (3) Incorporating the Levy flight search strategy, which aims to solve the shortcomings of traditional particle swarm algorithms that often fall into the suboptimal solution, it can be judged according to the evolutionary effect of the particle position, and if the particles are still unable to enter the more optimal position in many iterations, the Levy flight will be used to update the position of the particles, which is a strategy that increases the vitality of the particles. In summary, the IPSO-BP model proposed in this study has excellent predictive ability and, makes some positive contributions to the cause of air pollution prevention.

The atmospheric environment is of great importance to human health. However, its influencing factors are complex and variable. An efficient technique is required to more precisely estimate PM2.5 concentration values. In this paper, an enhanced Sparrow Search Algorithm (LASSA)-optimized Light Gradient Boosting Machine (LightGBM) is proposed for PM2.5 concentration prediction. This approach can provide accurate predictions while also reducing potential losses resulting from unexpected events. LightGBM is regarded as an outstanding machine learning approach; however, it includes hyperparameters that must be optimally mixed in order to achieve the desired results. We update the Sparrow Search Algorithm (SSA) and utilize it to identify the optimal combination of the most crucial parameters, using cross-validation to increase the reliability. Using limited air quality data and meteorological data as inputs, PM2.5 concentration values were predicted. The LASSA-LGB’s output was compared to normal LGB, SSA-LGB and ISSA-LGB. The findings demonstrate that LASSA-LGB outperforms the other models in terms of prediction accuracy. The RMSE and MAPE error indices were lowered from 3% to 16%. The concordance correlation coefficient is not less than 0.91, and the R2 reached 0.96. This indicates that the proposed model has potential advantages in the field of PM2.5 concentration prediction.

The delay and network are incorporated to describe the spatiotemporal behavior of a food-limited population dynamical system. By using the standard approach of upper and lower solutions, we have shown the global existence and uniqueness of solutions to the system. By analyzing eigenvalue spectrum, we show that the delay can cause the long-term behavior of the system from stability to instability, that is, the positive equilibrium is asymptotically stable in the absence of delay, but loses its stability such that the Hopf bifurcation occurs when the time delay increases beyond a threshold. By the norm form and the center manifold theory, we study the stability and direction of the Hopf bifurcation. We propose some formulas to control the stability and period of the bifurcating periodic solutions. Moreover, numerical simulations reveal that the network structure can switch the type of spatiotemporal patterns.

In this paper, we study the global existence and asymptotic dynamics of generalized magnetohydrodynamic equations in R 3 , in which the dissipation terms are - η ( - Δ ) α and - μ ( - Δ ) β , 0 < α , β < 1 . With the help of combining the local existence and the a priori estimates, we establish the global existence and uniqueness of solution with small initial data. Moreover, we obtain the asymptotic decay rates of solutions by the method of energy estimates.