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Although Ru‐based materials are among the outstanding catalysts for the oxygen evolution reaction (OER), the instability issue still haunts them and impedes the widespread application. The instability of Ru‐based OER catalysts is generally ascribed to the formation of soluble species through the over‐oxidation of Ru and structural decomposition caused by involvement of lattice oxygen. Herein, an effective strategy of selectively activating the lattice oxygen around Ru site is proposed to improve the OER activity and stability. Our synthesized spinel‐type electrocatalyst of Ru and Zn co‐doped Co3O4 showed an ultralow overpotential of 172 mV at 10 mA cm−2 and a long‐term stability reaching to 100 hours at 10 mA cm−2 for alkaline OER. The experimental results and theoretical simulations demonstrated that the lattice oxygen site jointly connected with the octahedral Ru and tetrahedral Zn atoms became more active than other oxygen sites near Ru atom, which further lowered the reaction energy barriers and avoided generating excessive oxygen vacancies to enhance the structural stability of Ru sites. The findings hope to provide a new perspective to improve the catalytic activity of Ru‐incorporated OER catalysts and the stability of lattice‐oxygen‐mediated mechanism. [ABSTRACT FROM AUTHOR]

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]

Summary: The current work was carried out to enhance the quality (including product colour, rehydration performance, textural properties, product cracking rate and sensory evaluation) of microwave freeze‐dried prepared taro balls, a popular starch‐based food product, by synergistic addition of guar gum and sodium bicarbonate. The results showed that taro balls with added sodium bicarbonate had the shortest freeze‐drying time (153 min), but the high‐cracking rate (66.67%) was not acceptable to consumers. The addition of guar gum could effectively reduce the cracking rate (17.5%) during freeze‐drying of taro balls, but did not significantly improve sensory evaluation and textural properties. The combination of sodium bicarbonate and guar gum demonstrated satisfactory positive effects in increasing the rehydration rate of freeze‐dried taro balls (53.67%), maintaining the whiteness (WI = 84.52) and reducing the cracking rate (21.67%). Although sodium bicarbonate alone produced the best results in terms of sensory ratings and textural properties, there was no significant difference between the effects of sodium bicarbonate alone and the combination of the two food additives on rehydrated taro balls. Therefore, the combination of sodium bicarbonate and guar gum is an appropriate hybrid strategy for producing high quality freeze‐dried taro balls. [ABSTRACT FROM AUTHOR]

Summary: The inherent poor cooking quality and poor texture of mixed‐grain pasta limit consumer acceptance. This study systematically investigated the effect of curdlan in the processing of sweet potato puree oat mixed‐grain noodles, and comprehensively analysed a number of key indicators such as cooking characteristics, moisture state, texture properties, microstructure, starch crystallinity characteristics, short‐range ordered structure and thermal properties. At the production practice level, the study clearly indicated that the 0.6% curdlan addition ratio was the best solution in sweet potato puree oat mixed‐grain noodles processing. This finding not only significantly reduces the fracture rate, water absorption rate and cooking loss rate during the cooking process, but also significantly improves the quality and yield of the product, and brings significant improvement in the taste and texture of the noodles, which is of great significance for industrial production. In the microstructural and molecular studies, we revealed how curdlan could enhance the structural stability and mechanical strength of noodles by promoting the uniform distribution of starch granules in the gluten protein network. Further, through X‐ray diffraction, ordered structure and thermal property analyses, we have found that colchicine can effectively regulate the interactions of hydrogen bonding between starch and the content of straight‐chain and branched‐chain starch, which significantly reduces the crystallinity, ordered short‐chain structure and enthalpy of pasting of starch in the pasta. These in‐depth research results not only help us to understand more comprehensively the formation mechanism of the modification of pasta quality by curdlan, but also provide a solid theoretical foundation and practical guidance for the improvement of pasta quality in mixed grains. [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]