Irrigation and nitrogen application rates have significant effects on greenhouse tomato yields, as well as water and nitrogen use efficiencies, but little is known regarding how these rates affect plant–microbiome interactions and how the associated changes might impact tomato yields. In this greenhouse study conducted over two years, the effects of three irrigation levels (moderate deficit with 65–75% water holding capacity threshold, slight deficit with 75–85%, and sufficient irrigation with 85–95%) and four nitrogen application levels (60, 120, 240, and 360 kg ha⁻¹) on tomato growth, yield, water and nitrogen productivities, and rhizosphere microbial diversities and functions were investigated. The results demonstrated that the highest tomato leaf area, dry biomass, yield, and water and nitrogen productivities were obtained under the treatment with sufficient irrigation. With increasing nitrogen application, the tomato leaf area, dry biomass, yield, and water and nitrogen productivities showed a trend of first increasing and then decreasing. Overall, the treatment (N2W3) with sufficient irrigation and 240 kg ha⁻¹ N was associated with the highest tomato growth, yield, and water and nitrogen productivities. Moreover, optimal irrigation and nitrogen application obviously altered the structures of rhizosphere bacterial and fungal communities, particularly recruiting microbiota conferring benefits to tomato growth and nitrogen fixation—namely, Lysobacter and Bradyrhizobium. Ultimately, optimal irrigation and nitrogen application significantly increased the relative abundances of functions related to carbon, sulfur, and nitrogen metabolism, especially nitrogen fixation. In summary, optimal irrigation and fertilization enhanced tomato yield, as well as water and nitrogen productivities by increasing the nitrogen fixation functions of the rhizosphere microbiome. Our results provide significant implications for tomato cultivation in greenhouses, in terms of optimized irrigation and fertilization.

In order to analyze the competitive relationship of different deformation mechanisms in wrought AZ31 magnesium alloy, the dynamic compressive experiments were conducted by a Split Hopkinson Pressure Bar (SHPB) apparatus and a resistance-heated furnace in the range of temperature between 20 and 350 °C at the strain rate of 1000 s⁻¹. With the help of Electron Backscattered Diffraction (EBSD) observation, theoretical calculated Schmid Factor (SF), Critical Resolved Shear Stress (CRSS), and critical equivalent stress (σ0.2), the dynamic compressive deformation behavior and corresponding mechanism of wrought AZ31 magnesium alloy along the normal direction (ND) were revealed in the current study. The results demonstrate that the c-axis of grains are gradually reoriented parallel to the normal direction of wrought AZ31-ND sheet with the temperature increasing, except the dynamic recrystallization (DRX) mechanism was activated or grains grew up. The non-basal slip and $\left\{10\overline{1}2\right\}$ tension twinning are respectively the predominant deformation mechanisms at lower temperatures (≤250 °C) and higher temperatures (≥250 °C). The predominant type of DRX mechanism of wrought AZ31-ND sheet is rotational dynamic recrystallization (RDRX), which is regarded as an obstacle for the kernel misorientation concentration region enhancement.

In order to investigate the effect of temperature on the microstructure evolution and mechanical response in the transverse direction of a wrought AZ31 (AZ31-TD) alloy under a high strain rate, the dynamic compression was conducted using Split Hopkinson Pressure Bar (SHPB) apparatus and a resistance-heated furnace under 1000 s⁻¹ at 20–250 °C. By combining optical and EBSD observations, the microstructure’s evolution was specifically analyzed. With the help of theoretically calculated Schmid Factors (SF) and Critical Resolved Shear Stress (CRSS), the activation and development deformation mechanisms are systematically discussed in the current study. The results demonstrated that the stress–strain curves are converted from a sigmoidal curve to a concave-down curve, which is caused by the preferentially and main deformation mechanism $\left\{10\overline{1}2\right\}$ tension twinning gradually converting to simultaneously exist with the deformation mechanism of a non-basal slip at an elevated temperature, then completing with each other. Finally, the dynamic recrystallization (DRX) and non-basal slip are largely activated and enhanced by temperature elevated to weaken the $\left\{10\overline{1}2\right\}$ tension twinning.

The performance of three widely used thermal radiation models, the P-1 model, the surface-to-surface (S2S) model and the Discrete-Ordinates (DO) model, were evaluated for simulation temperature in Chinses solar greenhouse. The thermal radiation models were evaluated by comparing the numerical results with experimental data at representative points in the CSG. For indoor rear wall, the indoor soil and indoor air, the models showed good agreement between the experimental data and the simulated results correspond to P1, S2S and DO respectively. This work provides information for simulate greenhouse temperature and use specific radiation models for the most suitable thermal environment for crop growth.

The growth environment of corps requires necessary improvements by Chinese solar greenhouses with Pad–Fan Cooling (PFC) systems for reducing their high temperatures in summer. Although computational fluid dynamics (CFD) could dynamically display the changes in humidity, temperature, and wind speed in solar greenhouses, its computational efficiency and accuracy are relatively low. In addition, the use of PFC systems can cool down solar greenhouses in summer, but they will also cause excessive humidity inside the greenhouses, thereby reducing the production efficiency of crops. Most existing studies only verify the effectiveness of a single machine learning (such as ARMA or ARIMA) or deep learning model (such as LSTM or TCN), lacking systematic comparison of different models. In the current study, two machine learning algorithms and three deep learning algorithms were used for their ability to predict a PFC system’s cooling effect, including on humidity, temperature, and wind speed, which were examined using Auto Regression Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Time Convolutional Network (TCN), and Glavnoe Razvedivatelnoe Upravlenie (GRU), respectively. These results show that deep learning algorithms are significantly more effective than traditional machine learning algorithms in capturing the complex nonlinear relationships and spatiotemporal changes inside solar greenhouses. The LSTM model achieves R² values of 0.918 for temperature, 0.896 for humidity, and 0.849 for wind speed on the test set. TCN showed strong performance in identifying high-frequency fluctuations and extreme nonlinear features, particularly in wind speed prediction (test set R² = 0.861). However, it exhibited limitations in modeling certain temperature dynamics (e.g., T6 test set R² = 0.242) and humidity evaporation processes (e.g., T7 training set R² = −0.856). GRU delivered excellent performance, achieving a favorable balance between accuracy and efficiency. It attained the highest prediction accuracy for temperature (test set R² = 0.925) and humidity (test set R² = 0.901), and performed only slightly worse than TCN in wind speed prediction. In summary, deep learning models, particularly GRU, offer more reliable methodological support for greenhouse microclimate prediction, thereby facilitating the precise regulation of cooling systems and scientifically informed crop management.

In order to enlarge and improve the application of phase changing materials (PCM) composite wall in Chinese solar greenhouse (CSG), the effect of thermal parameters on heat storage and release performance of PCM composite wall were systematically and scientifically investigated by available CFD code of commercial software ANSYS-Fluent, which was almost determined by the parameters such as density, thermal conductive, latent heat fusion and specific heat capacity. The numerical simulation was reasonably validated by the experimental result under the same condition, which was conducted by error analysis of interval analysis (IA) method. The result is shown that IA result between numerical simulation and experiment is 0.96, while the numerical simulation of PCM composite wall is significantly accurate and reliable. The maximum temperature of the center point in interior surface is completely dependent on the contrary tendency changing of thermal parameters at heating time, of which is directly proportional to thermal parameters changing at cooling time, except the specific heat capacity. While only the thermal conductivity increasing is benefit for increasing interior surface temperature of PCM composite wall at final cooling time. The effect of solely thermal parameter on the heat storage and release performance changing of PCM composite wall is from strength to weaken: density changing ([ ]) > thermal conductivity changing ([ ]) > latent heat fusion of liquid changing ([ ]) > specific heat capacity changing ([ ]).