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Effect of poultry manure and chemical fertilizers on soil chemical and biological properties in millet, sorghum, and maize
An improved UNet++ model for water body extraction of remote sensing images
Abstract Water body extraction in remote sensing is critical for environmental monitoring, flood control, and urban planning. However, the complex semantic information and varied morphology in remote sensing images present challenges for accurate extraction. Traditional semantic segmentation algorithms, with limited receptive fields, often fail to capture long-range dependencies, leading to misclassification and omissions. Additionally, most current methods rely on supervised learning, limiting generalization due to insufficient use of unlabeled data. This paper proposes ResDDSCUNet++, an improved UNet++ architecture for remote sensing water body extraction. The model replaces standard convolution and pooling layers with ConvNextV2 blocks, enhancing the capture of long-range dependencies. It also introduces the Attention Modulation Module (AMM) to focus on critical information and a residual depthwise separable double convolution module (ResDoubleDSC) to reduce model parameters and computational load. Furthermore, a hybrid training approach combining supervised and self-supervised learning is employed, leveraging unlabeled data for pretraining and fine-tuning on labeled data to improve generalization. Experimental results demonstrate that the proposed method outperforms other segmentation algorithms, improving the Dice coefficient by 1.6% and the IoU coefficient by 1.22%.
Steric hindrance of antibody binding in an Omicron spike fusion intermediate
Influences of different particle characteristics on particle movement in a forced vortex flow
Agro-based biochars combined with nitrogen fertilizer improve soil nutrient status and rice performance in contrasting soils of southern Nigeria
Abstract Agro-based biochars used together with mineral nitrogen fertilizer are increasingly recognized as climate-smart soil amendments for improving soil fertility and sustaining crop productivity. However, field information on the effects of rice husk biochar, sawdust biochar, and urea on post-harvest soil nutrient status across contrasting rice ecosystems in Nigeria remains limited. This study evaluated the effects of rice husk biochar, sawdust biochar, and urea on post-harvest soil nutrient status and rice performance in upland and lowland rice ecosystems established on Alfisol in Akure and Ultisol in Abakaliki, southern Nigeria. The experiment was conducted as a 2 × 2 × 4 factorial in a randomized complete block design with three replicates, involving two soil types, two rice ecologies/varieties, and four nutrient treatments: control, urea, rice husk biochar + urea, and sawdust biochar + urea. Soil pH, soil organic carbon (SOC), total nitrogen (TN), available phosphorus (P), exchangeable potassium (K), ammonium-N (NH 4 + -N), nitrate-N (NO 3 - -N), and rice yield components were assessed and analysed by factorial ANOVA, with mean separation using Tukey’s HSD at P < 0.05. Biochar-urea combinations improved post-harvest soil nutrient status and rice performance relative to the control and, in most cases, sole urea, with the strongest responses occurring in the 0–15 cm soil layer. Sawdust biochar + urea produced the highest grain yield of 4202.59 kg ha -1 , straw yield of 4999.21 kg ha -1 , and 1000-grain weight of 30.25 g, while rice husk biochar + urea produced statistically comparable grain and straw yields. These results indicate that locally available agro-based biochars can improve fertilizer effectiveness and post-harvest nutrient status under the conditions of this study; however, longer-term and rate-response studies are required before broad field recommendations are made.