The air pollution problem seriously affects economic development and people's health, and an efficient and accurate forecasting model of air quality will help manage air pollution problems. The comparison models chosen by other scholars are often based on derivative models of the proposed model and do not comprehensively compare other types of models with limited accuracy. In this paper, we establishes a combined ARIMA-CNN-LSTM model to predict the air quality index accurately. The model mainly consists of two parts: Using the ARIMA model to fit the linear part of the data and using the CNN-LSTM model to fit the nonlinear part of the data to avoid the problem of blindness in the CNN-LSTM hyperparameter setting. To avoid the dilemma of blindness in a CNN-LSTM hyperparameter setting, this article uses the Dung Beetle Optimizer tool to find the hyperparameters of a CNN-LSTM model, determine the best hyperparameters, and check the accuracy of the model. The proposed model is compared with other widely used models. The results show that the Dung Beetle Optimizer can effectively search for the optimal hyperparameters of the model and can solve the problem of blindness in setting the hyperparameters of the model. And the optimized ARIMA-DBO-CNN-LSTM model has higher predictive accuracy with stronger adaptability in predicting the three cities.

This paper proposed an elastodynamic modeling method combined with independent displacement coordinates and substructure synthesis technology. Firstly, the connecting rod was discretized, and the elastodynamic control equation for each element was established. The multipoint constraint element theory, linear algebra, and singularity analysis were used to identify the globally independent displacement coordinates of the manipulator. On this basis, the elastodynamic model using the substructure synthesis for the 3-PRS parallel manipulator (PM) was developed, with its natural frequencies distribution in the regular workspace discussed. The comparison with the finite-element results showed that the maximum error of the first three-order natural frequencies was within 1.03%, which verified the correctness of the analytical model. The proposed elastodynamic model included all the kinematic constraints of the manipulator without increasing the Lagrangian multiplier. The method is computationally efficient and assesses the dynamic behaviors of the mechanism at the predesign phase.

The pulp-molding product is a new degradable and pollution-free packaging material replacing traditional packaging materials. Countries are strongly recommending that pulp molding machines are mainly used to produce pulp-molding products. The stability, efficiency, and rapidity of its products’ molding and processing quality are largely controlled by the structural dynamic mechanics of the molding-machine frame. The first-order natural frequency, the fundamental frequency, is an important index to measure the dynamic performance of its equipment. The Pulp-Molding Machine Frame molding machine was taken as an example to analyze the dynamic performance and optimize the design in the work. Based on finite elements, the beam structure was divided into finite elements, and the global independent generalized displacement coordinates of the equipment were extracted using multi-point constraint elements. The elastic dynamic model of the device was established by combining the Lagrange equation and the global independent generalized displacement coordinates, and the correctness of the theoretical model was verified by the finite element method. Finally, taking the first natural frequency as the optimization-design goal, the section size of the beam in the equipment was optimized by the particle-swarm optimization algorithm. The results can be used to analyze the dynamic performance and optimize the design of the molding machine at the pre-design stage.

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