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Although mold designs are used in various sectors, molding composite materials results in issues such as deformation and insufficient stiffness. To this end, the response surface method was used to optimize the design of the mold support structure partition, improving the performance parameters of the mold by increasing the number of U and V direction partitions. The experiment showed that the deformation during forklift transportation decreased by 36.5%, during lifting transportation by 13.9%, during paving deformation by 36.9%, and during hot pressing, the deformation of the tank was reduced by 18.2%. After optimization, the maximum deformation of the mold under conditions such as forklift transportation, lifting, and paving is similar to or slightly reduced from the original model. Meanwhile, the thermal deformation and quality of the mold decreased by 28% and 12.8%, respectively. The research results have important reference value for the design of composite material forming molds, helping to improve the performance and efficiency of the molds and reduce costs. By optimizing the design of the support structure partition, effective control of mold deformation can be achieved, and the stiffness of the mold can be improved, thereby ensuring the stability and reliability of the mold under various working conditions.

Nonlinear disturbances, such as friction, backlash, and base vibration, are the main factors restricting the further improvement of the dynamic performance of the Acquisition, Tracking and Pointing (ATP) system. However, the modeling and compensation of the time-varying nonlinearities are still challenging. In this paper, a compound control algorithm based on model predictive control (MPC) and a Proportional Multiple-Integral State-Augmented Kalman Filter (PMISAKF) disturbance observer is proposed to improve the disturbance rejection performance. Specifically, a PMISAKF is used to estimate the system's state and external disturbances in real-time. These observed disturbances are then treated as known external inputs at the current or future time points and incorporated into the predictive model of MPC. This allows the optimization process to take these external factors into consideration, enabling the calculation of an optimal control strategy and achieving high pointing and tracking accuracy for nonlinear time-varying disturbances. Experimental results show that the proposed method can effectively improve the system's anti-interference ability and tracking accuracy. [ABSTRACT FROM AUTHOR]

In order to fully exploit the relationship between temporal features in photovoltaic power generation data and improve the prediction accuracy of photovoltaic power generation, a photovoltaic power generation forecasting method is proposed based on a hybrid model of the convolutional neural network (CNN) and extreme gradient boost (XGBoost). Taking the historical data of China's photovoltaic power plants as a sample, the high-dimensional mapping relationship of photovoltaic power generation variables is extracted based on the convolutional layer and pooling layer of the CNN network to construct a high-dimensional time-series feature vector, which is an input for the XGBoost. A photovoltaic power generation prediction model is established based on CNNXGBoost by training CNN and XGBoost parameters. Since it is difficult for a single model to achieve optimal prediction accuracy under different weather conditions, the k-means clustering algorithm is used to group the power datasets and train independent models to improve prediction accuracy. Through the actual data verification of photovoltaic power plants, the proposed photovoltaic power generation prediction model can accurately predict the power, which shows high prediction accuracy and generalization ability compared with other methods. [ABSTRACT FROM AUTHOR]

After a universal two-child policy was introduced in China in 2016, studies have been published using women's fertility intentions to forecast future births; however, the recommended algorithms need to be improved. In this study, an algorithm based on the method of limiting factors is developed to retrospectively forecast annual births in Xi'an City in the first three years of policy implementation, i.e. 2016–2018. The 2015 Xi'an Fertility Survey (sample: 560 one-child mothers) showed that 17% of mothers intended to have a second child, 30% were undecided and 53% did not intend to do so at the end of 2015. The low forecast variant based on the updated algorithm indicates that there would be a baby boom in 2016–2018, but the annual births would increase by 13% at most. The forecasting results are basically consistent with the official reports on annual births. This study emphasizes the importance of appropriately adjusting all fertility intentions in birth forecasting, helps to understand women's fertility behaviour and evaluate the effects of implementing the universal two-child policy, and has important implications for China's population and family planning work. [ABSTRACT FROM AUTHOR]

Objectives: This study provides an evolutionary perspective to a classic topic in demography, that is, the discrepancy between reproductive intention and subsequent behavior, in the context of China's two‐child policy. Methods: We conduct an event history analysis of longitudinal data from the 2015 and 2018 waves of the Xi'an Fertility Survey (sample size = 321 followed one‐child mothers) to test the hypotheses of how within‐family support/conflict affects women's fertility behavior. Results: Only 50% of positive intentions (i.e., intending to have a second child) led to another (live) birth within the 3‐year interval; meanwhile, 15% of uncertain intentions and 5% of negative intentions resulted in a birth. Husband's and the firstborn's emotional support raised the hazard of second childbirth along maternal life course, which cannot be fully mediated by mother's fertility intention and thus, contributed to an intention‐behavior gap. Husband's sibship size had dual effects on female childbearing behavior: A positive indirect effect mediated by fertility intention, but a negative direct effect presumably due to sibling competition for intergenerational support. Finally, after controlling for fertility intention, having a firstborn son was still associated significantly with a lower second‐childbirth hazard, presumably due to son preference as well as concern over parental investment. Conclusions: Our study identifies a discrepancy between maternal fertility intention and realized childbearing, which was partly explained by (lack of) support from other (multiple) stakeholders in family reproduction. [ABSTRACT FROM AUTHOR]