The accurate estimation of reference crop evapotranspiration (ET₀) is essential for crop water consumption modeling and agricultural water resource management. In the present study, three bionic algorithms (aquila optimizer (AO), tuna swarm optimization (TSO), and sparrow search algorithm (SSA)) were combined with an extreme learning machine (ELM) model to form three mixed models (AO-ELM, TSO-ELM, and SSA-ELM). The accuracy of the ET₀ estimates for five climate regions in China from 1970 to 2019 was evaluated using the FAO-56 Penman–Monteith (P-M) equation. The results showed that the predicted values of the three mixed models and the ELM model fitted the P-M calculated values well. R² and RMSE were 0.7654–0.9864 and 0.1271–0.7842 mm·d⁻¹, respectively, for which the prediction accuracy of the AO-ELM model was the highest. The performance of the AO-ELM combination5 (maximum temperature (T_max), minimum temperature (T_min), total solar radiation (R_s), sunshine duration (n)) was most significantly improved on the basis of the ELM model. The prediction accuracy for the stations in the plateau mountain climate (PMC) region was the best, while the prediction accuracy for the stations in the tropical monsoon climate region (TPMC) was the worst. In addition to the wind speed (U₂) in the temperate continental climate region (TCC)—which was the largest variable affecting ET₀—n, R_a, and total solar radiation (R_s) in the other climate regions were more important than relative humidity (RH) and wind speed (U₂) in predicting ET₀. Therefore, AO-ELM4 was selected for the TCC region (with T_max, T_min, R_s, and U₂ as inputs) and AO-ELM5 (with T_max, T_min, R_s, and n as inputs) was selected for the TMC, PMC, SMC, and TPMC regions when determining the best model for each climate region with limited meteorological data.

In this test, the waste concrete is pulverized, and the particles of appropriate diameter are selected as the recycled fine aggregate to replace the natural fine aggregate. Meanwhile, the test materials are processed in different ways, and are finally made into recycled concrete. The processing methods of test materials include: high temperature treatment of concrete particles, partial replacement of cement with fly ash, and replacement of water with sodium silicate solution. To process the materials is to study the influence of its single factor or the multiple coexistent factors on the compressive strength of recycled concrete, and then to explore the most effective method of recycled fine aggregate use under preset conditions. The results show that: 1) When the natural fine aggregate is replaced with the recycled fine aggregate and there is only a single variable factors, the compressive strength of the recycled concrete increase most obviously after the concrete particles are processed through a six-hour-long high temperature heat at 300°C, and when the replacement rate of recycled fine aggregate is about 35%, the strength value is the maximum; 2) When there are multiple variable factors in the test, the compressive strength of the recycled concrete is lower than that of the single variable factors.

This paper is devoted to describe the variation trend of temperature and humidity in the glasshouse in summer during the working of the four main cooling systems, i.e. natural ventilation system, mechanical ventilation system, wet curtain cooling system.Then, set up the quantitative relationship between the temperature and humidity by regression analysis method, as well as fit the variation relationship between the two factors by the use of linear function, logarithmic function, binomial distribution function, power function and exponential function. The result shows that the best fitting degree among the three cooling system is all by binomial function operational method, and the correlation coefficient of the binomial function is the highest one among the four, which reach: 0.9847, 0.9725 and 0.9813 respectively.