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Research on the evolutionary behavior of the particle breakage processes in coarse-grained soil under the action of train load is of practical significance for subgrade construction and maintenance. However, existing studies have not addressed the prediction of particle size distribution evolution. In this paper, the MTS loading system is used to simulate the dynamic train load effect on coarse-grained soil fillers. The study analyzes the influence of dynamic stress amplitude, loading frequency, and vibration times on both the macro-characteristics and micro-characteristics of particle breakage. The characteristics of particle fragmentation in coarse soil filler under high-speed train load are elucidated. Furthermore, a predictive model for the evolution of particle size distribution curves in relation to particle content and relative particle size is established using the ZHU continuous grading curve equation. This model captures the evolution process of particle breakage characteristics in coarse-grained soil fillers subjected to high-speed train loads. The applicability of this model has been verified. Based on the grading prediction model, an integral expression for the breakage rate index is derived, and the evolution characteristics of particle breakage in coarse-grained soil fillers under the action of train load are analyzed. The results indicate that during filling, the particle breakage mode of coarse-grained soil fillers during filling is primarily characterized by fracture and fragmentation; conversely, under dynamic cyclic loading conditions, it is predominantly characterized by fracture and grinding. The breakage rate aligns with the measured results, suggesting that the breakage rate index established in this study can effectively describe the evolution process of particle breakage in railway subgrade coarse-grained soil. After the reaching one million loading cycles, both deformation and particle breakage degree in coarse-grained soil fillers tend to stabilize. Under the action of dynamic stress amplitudes ranging from 10 to 200 kPa and loading frequencies between 2 and 12 Hz, the particle breakage index stabilizes below 1.1%. These research findings contribute to a deeper understanding of the evolutionary processes affecting engineering characteristics of railway subgrade coarse-grained soils and provide a theoretical as well as experimental foundation for railway subgrade construction and maintenance. [ABSTRACT FROM AUTHOR]

In this paper, a novel calculating method for the three-dimensional stress intensity factor (SIF) was put out. By using the finite element approach, it was possible to compute the Mode I, Mode II, and Mode III SIF under the impact of a single factor (height, width, thickness, crack length, and inclination angle) and derive its variation rule. The SIF was computed by using the uniform design method, which also yielded the fitting formula that could determine the fracture at any geometric position on any plate. Then, a new three-dimensional penetrating crack fracture criterion was established to forecast both Mode I, Mode II, and Mode III initiation parameters of three-dimensional penetrating oblique crack. Uniaxial compression tests were done in order to validate the forecasted outcomes, including the crack initiation angle and load. The results demonstrate that the normalized KI always decreases with z/t (the crack front spacing), while the normalized KII and KIII increase first and then decrease. KI and KIII increase with the crack length 2a, while KII decreases first and then increases with the crack length. KI and KII both increase with the plate height. KI rises with the crack inclination angle, while KII and KIII initially raise and then lower. Along with the crack inclination angle, the crack initiation angle decreases, and the crack initiation stress rises. All of fracture mechanisms are Mode I. [ABSTRACT FROM AUTHOR]

This study was aimed to investigate the effects of temperature and ultrasonic scaler on infusion process of green tea leaves, and catechins stability under ultrasonic vibration in green tea infusion (GTI). (−)-epigallocatechin (EGC), (−)-epicatechin gallate (ECG), (−)-catechin (C), (+)-epicatechin (EC), (−)-epigallocatechin gallate (EGCG) and (−)-gallocatechin gallate (GCG) were measured with ultra-high performance liquid chromatography (UHPLC). Green tea leaves were infused with distilled water (1:50 w/v) for 0.5–24 h at 25 °C, and 15 min at 65 °C respectively. Meanwhile, the infusion process of same ratio was conducted for 2 h at 25 °C, with or without assistance of ultrasonic scaler. After filtration of tea leaves, the tip of ultrasonic scaler was immersed into GTI and vibrated at 33 kHz for 2 h. More amounts of EGC, ECG, C, EC could be obtained in GTI (24 h, 25 °C) compared to the contrast (15 min, 65 °C), meanwhile, the concentration of EGCG could achieve 966.5 ± 2.9 μg/mL which accounted for 97.6% of that in the contrast. During the infusion process for 2 h, catechins amount could be improved by 20.8–31.2% with the ultrasonic assistance. After filtration of tea leaves, the amount of total catechins in GTI could be increased by 4.7% under ultrasonic vibration. Our results indicate that a high-quality GTI could be prepared at 25 °C and the level of catechins achieve a plateau phase after 3 h. The ultrasonic scaler could accelerate the infusion process, and didn't affect the catechins stability in GTI at 25 °C. [ABSTRACT FROM AUTHOR]

In this paper, a novel method for modeling and optimization of the contact angle of rice leaf surfaces by comparing the analytical result of the artificial neural network (ANN) and response surface methodology (RSM), to minimize the contact angle of the rice leaf surfaces. Three factors and three levels Box-Behnken design was used to analyze the external factors influencing the contact angle of the rice leaf surfaces, which consisted of temperature, humidity and the chemical pesticide concentration. By optimizing the designing parameters, the optimum input parameters were obtained. The best input conditions were temperature (25.96 °C), humidity (60.00%), chemical pesticide concentration (131.89 mg/L), and a minimum contact angle of 103.565° was obtained. Moreover, the ANN sensitivity analysis was used to study the weight of three external factors affecting the contact angle. Experiments showed the concentration of chemical pesticides to be the chief factor, as it has a weight is 55.61%, followed by humidity with a weight of 31.15% and temperature with 13.24%. Finally, the reliability of the RSM and ANN methods in modeling and predicting the contact angle was compared by analyzing the value of mean squared error (MSE) and determination coefficient (R2).The result showed that both models have better performance, and both RSM and ANN have better determination coefficients. The experiments also illustrated that the ANN method get better results than RSM in fitting experimental data, prediction and modeling. [ABSTRACT FROM AUTHOR]

Aiming to the disadvantages of short-term load forecasting with empirical mode decomposition (EMD) such as mode mixing and many high-frequency random components, a new short-term load forecasting model based on ensemble empirical mode decomposition (EEMD) and sub-section particle swarm optimization (SS-PSO) is proposed in this paper. Firstly, the load sequence is decomposed into a limited number of intrinsic mode function (IMF) components and one remainder by EEMD, which can avoid the mode mixing problem of traditional EMD. Then, through calculating and observing the spectrum of decomposed series, some low-frequency IMFs are extracted and reconstructed. Other IMFs can be forecasted with appropriate forecasting models. Since IMF1 is main random component of the load sequence, the linear combination model is adopted to forecast IMF1. Because the weights of the linear combination model are very important to obtain high forecasting accuracy, SS-PSO is proposed and used to optimize the linear combination weights. In addition, the factors such as temperature and weekday are taken into consideration for short-term load forecasting. Simulation results show that accuracy of the load forecasting model proposed in the paper is higher than that of BP neural network, RBF neural network, support vector machine, EMD and their combinations. [ABSTRACT FROM AUTHOR]

To study the first flush effect of nonpoint source pollution in the Guangzhou community unit, runoff from roads, roofs, and green spaces during three rainfall events was collected and analyzed for pollutants. Nine runoff pollution indices were considered. The dimensionless cumulative curve of pollutant mass vs. volume, the first flush coefficient (b) and the mass first flush ratio (MFFn) were used to assess the first flush effect of different underlying surfaces. The assessment results pointed out that the roof was most prone to first flush effect. And ammonia nitrogen and phosphorus were the main pollutants in the first flush in the study area. For a quantitative analysis of the first flush, the Storm Water Management Model (SWMM) was used to simulate the hydrological effect of low impact development (LID) implementation in the community. The results showed that the first flush strength was reduced after setting LID. And LID measures, such as green roofs and sunken green spaces, contribute to flood control and rainwater purification. This research can be relevant regarding for constructing sponge cities and reducing the pollution caused by the first flush. [ABSTRACT FROM AUTHOR]