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Abundant light and heat in the Hexi Oasis Irrigation Area in China provide superior natural conditions for agricultural development. To study the maize–soybean planting system of intercropping and determine superior group yield and economic benefits in the Hexi Oasis Irrigation Area, eight treatments were set up in 2022–2023: maize–soybean intercropping with a bandwidth of 1.8 m and a row ratio of 2:3 (M1S3), a bandwidth of 1.8 m and a row ratio of 2:4 (M1S4), a bandwidth of 2.0 m and a row ratio of 2:3 (M2S3), a bandwidth of 2.0 m and a row ratio of 2:4 (M2S4), a bandwidth of 2.2 m and a row ratio of 2:3 (M3S3), a bandwidth of 2.2 m and a row ratio of 2:4 (M3S4), monocropping maize (M), and monocropping soybean (S). We analyzed the effects of changes in bandwidth–row ratios on photosynthetic characteristics, yield, and interspecific relationships in these treatments during two crop reproductive periods. Our results showed the following: (1) Under the intercropping system, the photosynthetic capacity of maize was highest when the row ratio was 2∶3 and bandwidth was 1.8 m. The net photosynthetic rate (Pn) increased by 1.72% to 48.90%, the transpiration rate (Tr) increased by 5.53% to 118.10%, and stomatal conductance (Gs) increased by 2.82% to 86.49% compared with other planting systems. Increasing the bandwidth from 1.8 m to 2.2 m improved the photosynthetic characteristics of soybean, increasing Pn, Tr, and Gs by 3.44% to 74.21%, 3.92% to 53.69%, and 2.41% to 55.22%, respectively. (2) The yield of maize and soybean under monocropping was significantly higher than that under intercropping. In the intercropping treatments, the average yield of crops in the M3S3 system was 16,519.4 kg ha−1, an increase of 6.48% compared with the M3S4 system, indicating that the reduction of one row of soybean in the same bandwidth system increases crop yield; The average economic benefit of the M3S3 system over two years was 35,171.73 CNY ha−1, which increased by 13.3 and 80% compared with the average economic benefit of maize and soybean monocropping, indicating that the intercropping system leads to better economic results for farmers than monocropping. (3) In the two-year experiment, the land equivalent ratio (LER) was highest in the M3S3 model, averaging 1.25 over the two years, showing better land productivity compared with other intercropping systems. (4) When bandwidth was 1.8, 2.0, or 2.2 m, the LER decreased by 8.3, 5.9, and 5.6% when planting an additional row of soybeans, the relative crowding coefficient of soybeans in the respective bandwidths increased by 4.59, 4.72, and 0.75%, the competition ratio of maize (CRM) increased by 22.94, 16.97, and 12.74%, the competition ratio of soybean (CRS) decreased by 20.47, 17.61, and 16.78%, and the competitive power of maize was greater than that of soybean, indicating that the increase in soybean rows in the same bandwidth system would weaken the competitive advantage of soybean, resulting in crop yield and economic benefit reduction. When the row ratio was 2:3 or 2:4, bandwidth increased from 1.8 m to 2.2 m, LER decreased by 3.31 and 0.86%, intercropping maize aggressiveness (AM) decreased by 7.55 and 12.50%, CRM decreased by 18.04 and 24.84%, and CRS increased by 17.32 and 22.77%, respectively, which indicated that the increase in bandwidth under different row ratio systems could improve the competitive advantage of intercropping soybean, thereby improving crop yield and economic benefits. (5) The AHP method, entropy weight method, and TOPSIS analysis showed that M3S3 ranked first, with the highest comprehensive evaluation (0.6017). In conclusion, the M3S3 planting system can better coordinate crop interspecies relationships, with higher land yield and economic benefit, and can be used as a suitable maize–soybean intercropping system in the Hexi oasis irrigation area. [ABSTRACT FROM AUTHOR]

Systemic sclerosis (SSc), as an immune-mediated chronic connective tissue disease, is mainly characterized by fibrosis of multiple organs, including the skin and lungs. It is a rare disease, with a high incidence and mortality among rheumatic immune diseases. At present, there is no effective drug for the treatment of SSC. In recent years, it has been found that the interactions between T/B lymphocytes, fibroblasts, endothelial cells, and various pro-inffammatory and fibrotic signaling molecules play a crucial role in the occurrence and development of SSc. On this basis, a series of targeted drugs are currently being tested. CD20 monoclonal antibodies ( Rit- uximab), IL-6 receptor antagonists (Tocilizumab), and multi-target tyrosine kinase inhibitors ( Nintedanib) have been approved for the treatment of SSc or SSc-ILD by FDA. In addition, other targeted drugs such as Janus kinase inhibitors and various interleukin inhibitors have shown preliminary therapeutic potentials through clinical trials. This article reviews the research progress in targeted therapies for SSc by synthesizing the latest evidence from four aspects: targeting B lymphocytes, T lymphocytes, vascular endothelial cells, and pro-inffammatory and fibrotic signaling molecules. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated that deep learning‐based stereo matching methods (DLSMs) can far exceed conventional ones on most benchmark datasets by both improving visual performance and decreasing the mismatching rate. However, applying DLSMs on high‐resolution satellite stereos with broad image coverage and wide terrain variety is still challenging. First, the broad coverage of satellite stereos brings a wide disparity range, while DLSMs are limited to a narrow disparity range in most cases, resulting in incorrect disparity estimation in areas with contradictory disparity ranges. Second, high‐resolution satellite stereos always comprise various terrain types, which is more complicated than carefully prepared datasets. Thus, the performance of DLSMs on satellite stereos is unstable, especially for intractable regions such as texture‐less and occluded regions. Third, generating DSMs requires occlusion‐aware disparity maps, while traditional occlusion detection methods are not always applicable for DLSMs with continuous disparity. To tackle these problems, this paper proposes a novel DLSM‐based DSM generation workflow. The workflow comprises three steps: pre‐processing, disparity estimation and post‐processing. The pre‐processing step introduces low‐resolution terrain to shift unmatched disparity ranges into a fixed scope and crops satellite stereos to regular patches. The disparity estimation step proposes a hybrid feature fusion network (HF2Net) to improve the matching performance. In detail, HF2Net designs a cross‐scale feature extractor (CSF) and a multi‐scale cost filter. The feature extractor differentiates structural‐context features in complex scenes and thus enhances HF2Net's robustness to satellite stereos, especially on intractable regions. The cost filter filters out most matching errors to ensure accurate disparity estimation. The post‐processing step generates initial DSM patches with estimated disparity maps and then refines them for the final large‐scale DSMs. Primary experiments on the public US3D dataset showed better accuracy than state‐of‐the‐art methods, indicating HF2Net's superiority. We then created a self‐made Gaofen‐7 dataset to train HF2Net and conducted DSM generation experiments on two Gaofen‐7 stereos to further demonstrate the effectiveness and practical capability of the proposed workflow. [ABSTRACT FROM AUTHOR]

Lithium oxalate (Li2C2O4) is an attractive cathode pre-lithiation additive for lithium-ion batteries (LIBs), but its application is hindered by its high decomposition potential (>4.7 V). Due to the liquid–solid synergistic effect of the NaNO2 additive and the LiNi0.83Co0.07Mn0.1O2 (NCM) cathode material, the decomposition efficiency of micro-Li2C2O4 reaches 100% at a low charge cutoff voltage of 4.3 V. Our work boosts the widespread practical application of Li2C2O4 by a simple and promising electrolyte-assisted cathode pre-lithiation strategy. [ABSTRACT FROM AUTHOR]

Water scarcity and the excessive application of nitrogen fertilizer are key factors limiting the sustainable development of the hybrid seed maize industry in the oasis agricultural areas of the Hexi Corridor in China. To determine the optimal water–nitrogen management regime of hybrid seed maize, we established a field experiment in 2020–2021 with three irrigation quotas (W1, W2, and W3 were 60, 80, and 100% of the local conventional irrigation quota, respectively) and four nitrogen application levels (N0, N1, N2, and N3 were 0, 190, 285, 380 kg·hm−2). We analysed the influence of different water–nitrogen combinations on indices of seed vigour, yield, water use efficiency (WUE), irrigation water use efficiency (IUE), the partial productivity of nitrogen fertilizer (NFP), and the nitrogen fertilizer agronomic use efficiency (NFA) of hybrid seed maize. A comprehensive growth evaluation system for hybrid seed maize was established based on the AHP, entropy weight, and TOPSIS methods, and a coupled water–nitrogen response model for hybrid seed maize was established with the objectives of obtaining high-yield, efficient, and high-seed vigour. The results showed that the yield of hybrid seed maize, NFP, and NFA gradually increased with the increase in the irrigation amount, while IUE continuously decreased; the yield of hybrid seed maize, WUE, and NFA increased and then decreased, while NFP continuously decreased with an increase in the amount of nitrogen application. Further, treatment N2W3 had higher water and nitrogen use efficiency and the highest yield and seed viability with a yield of 9209.11 kg·hm−2 and germination percentage, germination index, and vigour index of 97.22, 58.91, and 1.55%, respectively. The model of the integrated growth response of hybrid seed maize to water–nitrogen showed that the combined benefits of the hybrid seed maize yield, WUE, and seed viability could be maximised in conjunction with the irrigation rate ranging from 3558.90 to 3971.64 m3·hm−2 and the fertiliser application rate of 262.20 to 320.53 kg·hm−2. This study can provide scientific guidance and act as a decision-making reference for the productive, efficient, and sustainable development of hybrid seed maize in the oasis agricultural area of the Hexi Corridor. [ABSTRACT FROM AUTHOR]

In order to unravel the effect of aerated irrigation on soil dissolved organic matter (DOM) fluorescence characteristics, and humification degree, a randomized block experiment was conducted with three factors and a two-level design, i.e., two irrigation rates (0.6 and 1.0 times of crop evaporation pan coefficient, W1 and W2), two nitrogen application rates (225 and 300 kg hm−2, N1 and N2), and two aeration rates (15% and 0% in control treatment, A1 and A0). Fluorescence regional integration (FRI) and correlation analysis methods were used to investigate the evolution characteristics of the soil DOM fluorescence spectrum. Under aerated and conventional subsurface irrigation, soil DOM components were dominated by humic acid-like substances, fulvic acid-like substances, tryptophan-like proteins, and supplemented by tyrosine-like proteins and dissolved microbial metabolites. Soil aeration could promote the consumption of soil DOM components under low irrigation rates and accelerate the consumption of soil DOM components under high irrigation rates. The humification index of AI treatments varied from 8.47 to 9.94 during the maturity growth stage of pepper, averagely increased by 31.59% compared with the non-aeration treatment. To sum up, aerated irrigation can promote the depletion of small molecular proteins and accelerate nutrient turnover and the accumulation of big molecular proteins. [ABSTRACT FROM AUTHOR]

Remote sensing image change detection (RSICD) is an essential measure for monitoring the earth's surface changes. In recent years, the explosive growth of very high-resolution (VHR) satellite sensors and the booming innovations in deep learning technology have significantly boosted RSICD development. However, most of the current RSICD models focus on locating accurate change areas while ignoring the efficiency of their method, which limits the practical application of RSICD models, especially for large-scale and emergency RSICD tasks. In this paper, we propose an Efficient Multi-scale-fusion Change Detection Network (EMS-CDNet) for bi-temporal RSICD tasks. Our EMS-CDNet pays more attention to the model's inference speed and the accuracy-efficiency trade-off rather than only pursuing detection accuracy. We designed a multi-scale fusion module for EMS-CDNet, which adopts multi-scale and multi-branch operations to extract multi-scale features simultaneously and aggregate features at different feature levels. In addition to EMS-CDNet's ability to achieve sufficient feature extraction, the multi-scale image input within the designed module alleviates the influence of image registration errors in practical applications, thereby strengthening EMS-CDNet's value for practical RSICD tasks. We also integrated a novel partition unit in EMS-CDNet to lighten the model while maintaining the detection ability of small targets, thus shortening its processing time without a severe accuracy decrease. We conducted experiments on two state-of-the-art (SOTA) public RSICD datasets and our own collected dataset. The public datasets were utilized to comparatively measure the overall accuracy and efficiency measurement of EMS-CDNet, and the dataset of images we collected was used to observe EMS-CDNet's performance under the influence of image registration errors. Our experimental results show that EMS-CDNet achieved a better accuracy-efficiency trade-off than the SOTA public datasets methods. For example, EMS-CDNet reduced the inference time by about 33% while maintaining identical detection accuracy to CLNet (the optimal method among the comparison methods). Furthermore, EMS-CDNet achieved higher accuracy on our collected dataset, with an F1 of 74% and mIoU of 0.806, demonstrating its robustness to image registration errors and showing its value for practical RSICD applications. [ABSTRACT FROM AUTHOR]

Owing to the thinness and large lateral size, 2D Si materials exhibit very promising prospects as the high‐performance anodes of lithium‐ion batteries (LIBs). However, the facile synthesis of ultrathin 2D Si nanosheets (Si‐NSs) and their efficient application still remain a great challenge. Herein, the fabrication of ultrathin Si‐NSs with the average thickness of <2 nm is demonstrated using a unique etching‐reduction protocol. After hybridizing with graphene, the as‐prepared Si‐NSs@rGO material delivers ultrahigh rate capability (2395.8 mAh g−1 at 0.05 A g−1 and 1727.3 mAh g−1 at 10 A g−1), long cycling lifespan (1000 cycles at 2 A g−1 with a capacity decay rate of 0.05% per cycle) and high average Coulombic efficiency (99.85% during 1000 cycles). The superior performance is attributed to the ultrathinness of Si‐NSs that greatly improves the diffusivity and reversibility of Li+ ions. This work provides a strategy for fabricating a high‐rate‐capability anode material to meet the growing demand for high power density LIBs. [ABSTRACT FROM AUTHOR]