In the field of mechanical motion, friction loss and material wear are common problems. As one of the essential components for enhancing the lubricating performance of gel-like lubricants, nano-additives leverage their unique physical and chemical properties to form an efficient protective film on friction surfaces. This effectively reduces friction resistance and inhibits wear progression, thereby playing a significant role in promoting energy conservation, emissions reduction, and the implementation of green development principles. This study first introduces the physical and chemical preparation processes of gel-like lubricant nanoadditives. It then classifies them (mainly based on metal bases, metal oxides, nanocarbon materials, and other nanoadditives). Then, the performance of gel-like lubricant nano-additives is evaluated (mainly in terms of anti-wear, friction reduction, oxidation resistance, and load carrying capacity), and the surface analysis technology used is described. Finally, we summarize the application scenarios of gel-like lubricant nano-additives, identify the challenges faced, and discuss future prospects. This study provides new insights and directions for the design and synthesis of novel gel-like lubricants with significant lubricating and anti-wear properties in the future.

This study aimed at investigating the applicability of a SWAT (Soil and Water Assessment Tool) model in understanding the effects of drought on winter wheat. This study is based on the water requirement of the crop and addresses the absence of a SWAT model with real-time irrigation simulation. A real-time irrigation module was developed for the downstream irrigation area of the Yellow River to estimate the real-time irrigation of crops. By further simulating the dynamic evolution process of soil moisture content, a dynamic drought evaluation model of winter wheat was established, and the relative soil moisture was set as the evaluation index to assess and analyze the dynamic variation of drought evolution during the growth of winter wheat. The results showed that the improved SWAT model has strong applicability. During the growth of winter wheat, the variation trend of droughts is consistent with that of natural precipitation. For winter wheat, drought is quite normal during all growth stages, and the average frequency is 93.5%, 89.1%, 84.8%, 89.1% and 95.7%. Severe drought and extra-severe drought usually occur during the sowing–tillering stage and the tillering–greening stage, and the frequency of occurrence is 30.4% and 19.6%, respectively. This paper provides a novel approach for the study of agricultural drought in the downstream irrigation area of the Yellow River.

As global warming progresses, quantifying drought thresholds for crop yield losses is crucial for food security and sustainable agriculture. Based on the CNN-LSTM model and Copula function, this study constructs a conditional probability framework for yield losses under future climate change. It analyzes the relationship between the Standardized Precipitation–Evapotranspiration Index (SPEI) and winter wheat yield, assesses the vulnerability of winter wheat in various regions to drought stress, and quantifies the drought thresholds under climate change. The results showed that (1) SPEI in Zhoukou, Sanmenxia, and Nanyang was significantly correlated with yield; (2) the drought vulnerability of southern and eastern was higher than that of center, western, and northern in the past (2000–2023) and future (2024–2047); (3) there were significant differences in drought thresholds. The yield loss of winter wheat below 30, 50, and 70 percentiles in southern and eastern (past/future) were −1.86/−2.47, −0.85/−1.39, and 0.60/0.35 (Xinyang); −1.45/−2.16, −0.75/−1.34, −0.17/−0.43 (Nanyang); −1.47/−2.24, −0.97/−1.61, 0.69/0.28 (Zhoukou); −2.18/−2.86, −1.80/−2.36, −0.75/−1.08 (Kaifeng), indicating that the drought threshold will reduce in the future. This is mainly due to the different climate and soil conditions in different regions of Henan Province. In the context of future climate change, droughts will be more frequent. Hence, the research results provide a valuable reference for the efficient utilization of agricultural water resources and the prevention and control of drought risk under climate change in the future.

Maize is vital for global and Chinese food security. Yet, in Henan Province, a key maize-growing region in China, water scarcity, uneven rainfall, and inefficient irrigation and fertilization limit its yield and quality. This study combines a two-year field experiment (2023–2024) with the DSSAT model to optimize irrigation and fertilization for typical hydrological years (wet, normal, and dry). After calibration and validation with field data, the DSSAT model showed strong performance. Results indicate that optimal irrigation timing and volume vary with hydrological years: no irrigation is needed in wet years, one 30 mm irrigation at the tasseling (VT) stage in normal years, and three irrigations (total 90 mm) at the emergence (VE), jointing (VT), and grain filling (R2) stages in dry years. The optimal nitrogen fertilizer is 240 kg·ha⁻¹ in water-rich and normal years and 180 kg·ha⁻¹ in dry years. These optimized schemes can achieve 98–100% of maximum potential maize yields across hydrological years, offering practical insights for enhancing agricultural water and nutrient management in central Henan to support sustainable development and reduce environmental impacts.

Identifying a water–nitrogen coupling strategy to achieve high efficiency, emission reduction, and optimal yield in summer maize under multi-objective conditions is crucial for enhancing nitrogen fertilizer utilization and promoting agricultural sustainability. This study conducted a field experiment on water–fertilizer coupling in summer maize, with three irrigation levels (60%θ_f, 70%θ_f, 80%θ_f, with θ_f representing field capacity) and four nitrogen application levels (0, 180, 270, 360 kg/ha). It analyzed variations in yield, partial factor productivity of nitrogen fertilizer (PFP_N), and the soil CO₂ emission flux across different water–nitrogen combinations, establishing a multi-vector optimization model. NSGA-III (non-dominated Sorting Genetic Algorithm III) was utilized to determine the most effective combination of water and nitrogen. The results indicated that maize yield initially increases and then declines as irrigation and nitrogen levels rise. PFP_N showed a decreasing trend, and its decline gradually decreased with increasing irrigation levels, suggesting that water can alleviate nitrogen stress to some extent. Soil carbon dioxide exhalation intensity increased with both irrigation and nitrogen levels. The NSGA-III optimization revealed that the optimal water–nitrogen ratio is 1086.28 m³/ha for irrigation and 265.79 kg/ha for nitrogen. Compared with the best water–nitrogen combination (W2N3) from the experiment, this optimized scheme showed no significant difference in irrigation volume, yield, or soil CO₂ emission flux while increasing PFP_N by 13.46% and saving 1.56% of nitrogen fertilizer. In summary, the optimized water–fertilizer coupling scheme provides a scientific basis for high-efficiency, high-yield, and low-emission maize production in Henan Province, supporting sustainable agricultural development.

Accurate and reliable estimation of reference crop evapotranspiration (ET₀) in the North Henan Plain is crucial for agricultural water resource management, production, and food supply in China. This study aims to evaluate the performance of deep learning (DL) methods in ET₀ estimation and assess the applicability of the developed DL model beyond the training domain. This study utilized historical meteorological data from Zhengzhou City, northern Henan, spanning 2010–2024. Meteorological variables were selected through correlation analysis and maximum information coefficient (MIC). A novel DL model—the TCN-Attention model (TA)—was constructed by incorporating a self-attention mechanism into the temporal convolutional network (TCN) model. This model was compared with two classical DL models—Long Short-Term Memory (LSTM) and TCN. Results indicate: (1) Sunshine duration ($n$), relative humidity ($\mathrm{R}\mathrm{H}$), and maximum temperature (T_max) are the three most significant features influencing summer maize evapotranspiration; (2) prediction accuracy under the same input scenarios: TA model > TCN model > LSTM model; (3) in scenarios where only temperature data is input, the TA model has the highest prediction accuracy, surpassing the H-S empirical method; and (4) for limited meteorological data, the combination of temperature and humidity was found to be most effective, showing good adaptability and accuracy at different time steps (hourly: R² = 0.982; daily: R² = 0.975; weekly: R² = 0.928). This study highlights the potential of the TA model for estimating reference crop evapotranspiration in the northern Henan Plain, which may provide theoretical guidance for crop irrigation management under future climate change.