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In the image Gaussian filtering process, convolving with a Gaussian matrix is essential due to the numerous arithmetic computations involved, predominantly multiplications and additions. This can heavily tax the system's memory, particularly with frequent use. To address this issue, a W/Ta2O5/Ag memristor was employed to substantially mitigate the computational overhead associated with convolution operations. Additionally, an interlayer of ZnO was subsequently introduced into the memristor. The resulting Ta2O5/ZnO heterostructure layer exhibited improved linearity in the pulse response, which enhanced linearity facilitates easy adjustment of the conductance magnitude through a linear mapping of the number of pulses and the conductance. Subsequently, the conductance of the W/Ta2O5/ZnO/Ag bilayer memristor was employed as the weights for the convolution kernel in convolution operations. Gaussian noise removal in image processing was achieved by assembling a 5 × 5 memristor array as the kernel. When denoising was performed using memristor arrays, compared to denoising achieved through Gaussian matrix convolution, an average loss of less than 5% was observed. The provided memristors demonstrate significant potential in convolutional computations, particularly for subsequent applications in convolutional neural networks (CNNs).

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Drought is one of the vital meteorological disasters that influence crop growth. Timely and accurately estimating the drought dynamics of crops is valuable for decision-maker to formulate scientific management measures of agricultural drought risk. In this study, the evapotranspiration and drought dynamics of winter wheat from 1981 to 2020 in the Huang-Huai-Hai (HHH) region of China were evaluated based on long-term multi-source observation data. Four key developmental stages of winter wheat were given attentions: growth before winter stage, overwintering stage, stage of greening-heading, and stage of filling-maturity. The crop water deficit index (CWDI) on a daily scale was established for quantitatively appraising the impacts of drought on winter wheat. Our results indicated that interannual variation in reference crop evapotranspiration (ET0) during the growth season of winter wheat from 1981 to 2020 in the HHH region showed a slight increase trend, with an average of 602.4 mm and obvious spatial differences of decreasing from the Northeast to the Southwest. Over the past forty years, the winter wheat in the HHH region was most severely affected by severe drought, followed by moderate drought, and finally mild drought. In addition, the impacts of drought on winter wheat at different critical growth stages varied greatly. For the growth before winter stage, the winter wheat was mainly threatened by mild, moderate, and severe droughts. For the overwintering stage, the winter wheat was mainly threatened by moderate, severe, and extreme droughts. For the greening-heading stage, the winter wheat was mainly threatened by mild, moderate, severe, and extreme droughts. For the filling-maturity stage, the winter wheat was mainly threatened by mild and moderate droughts. Finally, the impacts of drought on winter wheat during 1981-2020 in the HHH region were revealed to differ extraordinarily in space. In particular, the areas of winter wheat affected by severe drought significantly decreased. However, the areas of winter wheat affected by moderate drought clearly expanded. Our findings provide new insights for further improving climate change impact studies and agricultural drought defense capabilities adapting to continuous environmental change.

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Capitalizing on rapid carrier migration characteristics and outstanding photoelectric conversion performance, halide perovskite memristors demonstrate an exceptional resistive switching performance. However, they have consistently faced constraints due to material stability issues. This study systematically employs elemental modulation and dimension engineering to effectively control perovskite memristors with different dimensions and A-site elements. Compared to pure 3D and 2D perovskites, the quasi-2D perovskite memristor, specifically BA0.15MA0.85PbI3, is identified as the optimal choice through observations of resistive switching (HRS current < 10-5 A, ON/OFF ratio > 103, endurance cycles > 1000, and retention time > 104 s) and synaptic plasticity characteristics. Subsequently, a comprehensive investigation into various synaptic plasticity aspects, including paired-pulse facilitation (PPF), spike-variability-dependent plasticity (SVDP), spike-rate-dependent plasticity (SRDP), and spike-timing-dependent plasticity (STDP), is conducted. Practical applications, such as memory-forgetting-memory and recognition of the Modified National Institute of Standards and Technology (MNIST) database handwritten data set (accuracy rate reaching 94.8%), are explored and successfully realized. This article provides good theoretical guidance for synaptic-like simulation in perovskite memristors.

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Forests play a key role in the regional or global carbon cycle. Determining the forest carbon budget is of great significance for estimating regional carbon budgets and formulating forest management policies to cope with climate change. However, the carbon budget of Chinese different forests and their relative contributions are not completely clear so far. We evaluated the carbon budget of different forests from 1981 to 2020 in China through combining model with remote sensing observation. In addition, we also determined the relative contribution of carbon budget of each forest type to all forests in China. Eight forest types were studied: evergreen coniferous forest (ECF), deciduous coniferous forest (DCF), coniferous and broad-leaved mixed forest (CBF), deciduous broad-leaved forest (DBF), evergreen broad-leaved forest (EBF), evergreen deciduous broad-leaved mixed forest (EDBF), seasonal rain forest (SRF), and rain forest (RF). The results indicated that the Chinese forests were mainly carbon sink from 1981 to 2020, particularly the annual average carbon budget of forest from 2011 to 2020 was 0.191 PgC·a-1. Spatially, the forests' carbon budget demonstrated obvious regional differences, gradually decreasing from Southeast China to Northwest China. The relative contributions of carbon budget in different forests to all forests in China were different. During 2011-2020, the ECF forests contributed the most carbon budget (34.45%), followed by DBF forests (25.89%), EBF forests (24.82%), EDBF forests (13.10%), RF forests (2.23%), SRF forests (3.14%) and CBF forests (1.14%). However, the DCF forests were found mainly as carbon source. These results contribute to our understanding of regional carbon budget of forests.

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