Winter extreme low temperature events have been occurring frequently both before and after the winter season. The freezing resistance temperature of wheat is far lower than the intensity of low temperatures during the mid-winter period. Therefore, it is necessary to further quantify and evaluate the impact of low-temperature periods and durations during the early winter and the green-up period on the freezing resistance of wheat, based on different evaluation indicators. Through conducting experiments in an artificial low-temperature control chamber, this study investigates the critical temperature thresholds for the impact of different low-temperature periods and durations on the tiller and yield of winter wheat, as well as the critical temperature thresholds for soil effective negative accumulated temperature. The results demonstrate that (1) the tiller mortality rate (RT) and yield reduction rate (RY) of winter wheat during the winter increase with the severity and duration of low temperatures, showing an S-shaped curve. The winter wheat mortality rate during the early winter is related to the soil effective negative accumulated temperature in an exponential function, while the mid-winter and green-up stages have a linear relationship. (2) The freezing threshold temperatures for the RT, RY and soil negative accumulated temperature (SENAT) in different low-temperature periods (early winter, mid-winter, and green-up periods) range from - 11.7 to -17.9 degrees C, -9.4 to -15.6 degrees C, and 15.9 to 131.7 degrees Ch (2.2 to 16.8 degrees Cd), respectively. (3) The freezing threshold temperatures for the RT and RY in different low-temperature durations (1 day, 2 days, and 3 days) range from - 2.8 to -17.9 degrees C and - 9.4 to -15.6 degrees C, respectively. The findings of this study provide technical support and scientific guidance for the global cultivation structure and variety layout of winter wheat under the background of climate warming, as well as for the prevention and reduction of freezing damage and yield losses.

In the course of pipe jacking construction, the carrying-soil effect frequently arises, influenced by factors such as excavation unloading, ongoing disturbance from successive pipe sections, and the progressive accumulation of soil adhesion. The pipe jacking slurry serves as a critical agent for friction reduction and strata support, essential for the secure advancement of the construction process. This study introduces the Microbial-Induced Calcium Carbonate Precipitation (MICP) technology into the realm of pipe jacking slurry, aiming to enhance its friction-reduction capabilities and the stability of the soil enveloping the pipe. An optimal MICP-slurry formulation was determined using the uniform design approach. Subsequent model tests were carried out to assess the friction-reducing efficacy of the MICP-slurry, while the mechanism by which the MICP-slurry reinforces strata stability was investigated through soil mechanics and scanning electron microscopy (SEM) analyses. The findings indicate that the optimal MICP-slurry composition is as follows: bentonite: sodium carboxymethyl cellulose: soda ash: polyacrylamide: xanthan gum = 12%: 0.31%: 0.36%: 0.25%: 0.54%. The MICP-slurry achieves a 42.2% reduction in the friction coefficient between the test block and the sand. In comparison with the untreated sample, the cohesion of the MICP-treated sample is enhanced by 38.12%, and the internal friction angle increases by 14.01%. SEM examination reveals that the calcium carbonate crystals precipitated by the MICP-slurry within the soil populate the pores, increase the inter-particle bite force, and bolster the soil's mechanical characteristics.

Winter wheat (Triticum aestivum L.) is a crucial crop that guarantees food supply in the North China Plain (NCP). As the frequency of extreme cold events increases, it is necessary to explore the freezing resistance of different wheat varieties in order to clarify planting boundaries and help with risk assessment. In this study, 2-year controlled experiments were conducted to explore the effect of freezing temperatures (T air) and freezing durations on three winterness types. A set of indexes were used to characterize the subfreezing stress on wheat tiller, leaf, and final yield. Logistical regressions were used to quantify the temperature threshold for 10%, 30%, and 50% of freezing injury. The results showed that the lower temperature threshold of tiller (LT) varied from -9.6 to -15.9 degrees C, -10.7 to -19.1 degrees C and -11.4 to -21.2 degrees C for LT10, LT30, and LT50, respectively. The difference between LT and yield loss (YL) indexes reduced with decreased winterness types and was -0.1 to 3.4 degrees C, -0.7 to 2.1 degrees C, and 0.3 to 0.9 degrees C higher compared with YL thresholds for winterness, semi-winterness, and weak-winterness types, respectively. The average minimum soil temperature was 7.5, 4.8, and 4.2 degrees C higher than T-air for 1-, 2-, and 3-day treatment, respectively. Soil effective negative accumulated temperature hours (TSEh) ranged from 6.9 to 12.0, 48.4 to 6.9, and 84.7 to 106.9 degrees Ch for 10%, 30%, and 50% tiller mortality, respectively. Freezing treatment with T-air < -12, -9, and -8 degrees C obviously decreased leaf Fv/Fm for the three varieties and Fv/Fm declined obviously after 5 days of recovery under field conditions. Our results provided multiple indexes for quantifying subfreezing damage in practical wheat production and could shed light on future risk assessment.

The seismic events on February 6, 2023, in the province of Kahramanmaras,/T & uuml;rkiye, caused severe damage and the collapse of numerous structures due to underlying soil issues. This catastrophe revealed the inevitable requirement to evaluate the effect of soil profile on structural safety. In the present study, novel artificial intelligence (AI) functions based on the three-dimensional finite element (3D FE) method considering various soil parameters were developed to predict the effects of earthquakes. A 3D FE model of the ten-story building with a known soil profile and structural elements was created in the first stage, accounting for the soil-pile-structure interaction. After model validation, numerous parametric time history earthquake analyses were performed using the February 6 Pazarc & imath;k/Kahramanmaras, (Mw = 7.7) earthquake records. Therefore, the effects of soil parameters on acceleration, settlement, and lateral deformations were investigated. An innovative coding infrastructure, leveraging the power of AI, was developed to generate optimal network solutions automatically for creating high-order regression prediction functions. The 3D FE data was integrated into the code, and subsequently, an artificial neural network was utilized to formulate a function that yielded statistically significant outcomes. The created function accurately predicted the accelerations, settlements, and deformations. A novel method for indicating the potential deformations and accelerations inflicted by earthquakes based on soil parameters was introduced. This methodology can serve as a practical guide for researchers and project implementers in the initial design phases.