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The present study tested the effect of addition of whole common buckwheat flour on the rheological properties of wheat dough, and the textural characteristics, water distribution, secondary structure and molecular mass distribution of proteins, microstructure, and color of fresh noodles. The results demonstrated that the dough with 30% whole common buckwheat flour had a higher G’ (5.3 × 105 Pa) and a minimum tan δ value of 0.50. The resulting noodles had a maximum elasticity of 0.97, a maximum α-helix content of 28.30%, and a denser and more homogenous microstructure. According to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) data, the band intensities of buckwheat protein subunits increased with increasing addition of whole common buckwheat flour. Compared with the control group, the addition of whole common buckwheat flour caused partial transformation of tightly bound water to weakly bound water and free water. As the addition of common buckwheat flour increased, the L* value of noodles decreased significantly, the a* and b* values increased to varying degrees, and as a result the overall color turned reddish and yellow. In conclusion, the addition of 30% whole common buckwheat flour can improve the mechanical properties and processing quality of fresh noodles.

Objective: Determine the appropriate soymilk concentration and slurry depth to provide a theoretical reference for the efficient and high-quality production of yuba. Methods: The effect of soymilk solids concentration and slurry depth on the yield, film-forming rate, nutrient contents, mechanical properties, cooking loss rate, and rehydration ratio were investigated. Results: The optimal yield solids of yuba (34.68%) and highest nutritional value occurs when the soymilk solids concentration is 6% and the soymilk slurry depth is 6 cm, which presents 51.05% protein, 23.42% fats, and it also has good tensile strength and rehydration qualities, with respective measurements of 3.74 MPa and 3.80 g/g. Conclusion: Considering various factors such as yield, film formation rate and overall food quality, it is recommended to employ processing conditions with a soymilk solids concentration of 6% and a slurry depth of 6 cm for the efficient and high-quality production of yuba.

To analyze the effect of heat treatment on the structure and physicochemical properties of buckwheat bran protein (BBP), the molecular mass, secondary structure, intrinsic fluorescence spectrum, free sulfhydryl content, disulfide bond content, microstructure, thermal properties, and rheological properties of BBP were characterized before and after heat treatment. The results showed that part of the subunits of BBP were degraded into small polypeptides or oligopeptides. The α-helical structure was transformed into β-sheet, β-turn and random coil, and the contents of free sulfhydryl groups and disulfide bonds were significantly increased. Compared with soy protein isolate (SPI), the peak temperature, denaturation enthalpy, apparent viscosity, storage modulus (G’) and loss modulus (G”) of BBP were significantly lower. The surface of heat-induced gels of BBP was rougher, and there were obvious cracks on the gel wall, indicating that the thermal stability of BBP was lower than that of SPI. In summary, heat treatment could change the structure of BBP, and have different effects on its physicochemical properties.

This study aims to tackle the problem of frequent coal-rock dynamic events caused by local stress concentration in deep mining working face under high static stress environment. Specifically, by taking the deep working face of Hongqinghe Mine located in Xinjie Mining Area as an example for analysisthis paper investigates the geological conditions of No. 3-1coal seam that are affected by the mining method and the coal rock dynamic disaster events occurred during mining; utilizes numerical simulation to simulate the mining stress field and energy field of the stope; analyzes the potential coal burst instability area and mechanism of coal burst gestation and catastrophe in the stope during the mining of the No. 3-1103 working face; probes into the sources of catastrophic stress and influencing factors of coal-rock disasters in dangerous areas, puts forward targeted prevention and control measures. Results show that: ① the roof strata structure, goaf and coal pillar are the key factors influencing coal burst in the stope during the mining of the No. 3-1103 working face; ② the simulation results indicatethe location and characteristics of the coal burst instability area in the stope of the No. 3-1103 working faces, as well as the simulation outlines 5 strong coal burst areas and 11 medium coal burst areas presented in the stope; ③ Measures are proposed for coal burst during mining from the perspective of cutting off the dynamic load source, reducing static load stress concentration, and blocking the transmission of high concentration stress. Detailed measures include implementing multi-source prevention and control in areas with high static or high static+dynamic loads, implement pre-pressure off-load measures prior to mining, and local risk relief and coal burst prevention and reduction treatments during mining.

Accurately estimating shrub biomass in arid and semi-arid regions is critical for understanding ecosystem productivity and carbon stocks at both local and global scales. Due to the short and sparse features of shrubs, capturing the shrub biomass accurately by satellite observations is challenging. Previous studies mostly used ground samples and satellite observations to estimate shrub biomass by establishing a direct connection, which was often hindered by the limited number of ground samples and spatial scale mismatch between samples and observations. Unmanned aerial vehicles (UAVs) provide opportunities to obtain more samples that are in line with the aspects of satellite observations (i.e., scale) for regional-scale shrub biomass estimations accurately with low costs. However, few studies have been conducted based on the air-space-ground-scale connection assisted by UAVs. Here we developed a framework for estimating 10 m shrub biomass at a regional scale by integrating ground measurements, UAV, Landsat, and Sentinel-1/2 observations. First, the spatial distribution map of shrublands and non-shrublands was generated in 2023 in the Helan Mountains of Ningxia province, China. This map had an F1 score of 0.92. Subsequently, the UAV-based shrub biomass map was estimated using an empirical model between the biomass and the crown area of shrubs, which was aggregated at a 10 m × 10 m grid to match the spatial resolution of Sentinel-1/2 images. Then, a regional-scale estimation model of shrub biomass was developed with a random forest regression (RFR) approach driven by ground biomass measurements, UAV-based biomass, and the optimal satellite metrics. Finally, the developed model was used to produce the biomass map of shrublands over the study area in 2023. The uncertainty of the resultant biomass map was characterized by the pixel-level standard deviation (SD) using the leave-one-out cross-validation (LOOCV) method. The results suggested that the integration of multi-scale observations from the ground, UAVs, and satellites provided a promising approach to obtaining the regional shrub biomass accurately. Our developed model, which integrates satellite spectral bands and vegetation indices (R2 = 0.62), outperformed models driven solely by spectral bands (R2 = 0.33) or vegetation indices (R2 = 0.55). In addition, our estimated biomass has an average uncertainty of less than 4%, with the lowest values (<2%) occurring in regions with high shrub coverage (>30%) and biomass production (>300 g/m2). This study provides a methodology to accurately monitor the shrub biomass from satellite images assisted by near-ground UAV observations as well as ground measurements. [ABSTRACT FROM AUTHOR]

Performing remote sensing scene classification (RSSC) directly on satellites can alleviate data downlink burdens and reduce latency. Compared to convolutional neural networks (CNNs), the all-adder neural network (A2NN) is a novel basic neural network that is more suitable for onboard RSSC, enabling lower computational overhead by eliminating multiplication operations in convolutional layers. However, the extensive floating-point data and operations in A2NNs still lead to significant storage overhead and power consumption during hardware deployment. In this article, a shared scaling factor-based de-biasing quantization (SSDQ) method tailored for the quantization of A2NNs is proposed to address this issue, including a powers-of-two (POT)-based shared scaling factor quantization scheme and a multi-dimensional de-biasing (MDD) quantization strategy. Specifically, the POT-based shared scaling factor quantization scheme converts the adder filters in A2NNs to quantized adder filters with hardware-friendly integer input activations, weights, and operations. Thus, quantized A2NNs (Q-A2NNs) composed of quantized adder filters have lower computational and memory overheads than A2NNs, increasing their utility in hardware deployment. Although low-bit-width Q-A2NNs exhibit significantly reduced RSSC accuracy compared to A2NNs, this issue can be alleviated by employing the proposed MDD quantization strategy, which combines a weight-debiasing (WD) strategy, which reduces performance degradation due to deviations in the quantized weights, with a feature-debiasing (FD) strategy, which enhances the classification performance of Q-A2NNs through minimizing deviations among the output features of each layer. Extensive experiments and analyses demonstrate that the proposed SSDQ method can efficiently quantize A2NNs to obtain Q-A2NNs with low computational and memory overheads while maintaining comparable performance to A2NNs, thus having high potential for onboard RSSC. [ABSTRACT FROM AUTHOR]

The vibration signals collected by acceleration sensors are interspersed with noise interference, which increases the difficulty of fault diagnosis for rolling bearings. For this reason, a rolling bearing fault diagnosis method based on complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) and improved convolutional neural network (CNN) is proposed. Firstly, the original vibration signal is decomposed into a series of intrinsic modal function (IMF) components using the CEEMDAN algorithm, the components are filtered according to the correlation coefficients and the signals are reconstructed. Secondly, the reconstructed signals are converted into a two-dimensional grey-scale map and input into a convolutional neural network to extract the features. Lastly, the features are inputted into a support vector machine (SVM) with the optimised parameters of the grey wolf optimiser (GWO) to perform the identification and classification. The experimental results show that the rolling bearing fault diagnosis method based on CEEMDAN and CNN-SVM proposed in this paper can significantly reduce the noise interference, and its average fault diagnosis accuracy is as high as 99.25%. Therefore, it is feasible to apply it in the field of rolling bearing fault diagnosis. [ABSTRACT FROM AUTHOR]