SPANet: A Self-Balancing Position Attention Network for Anchor-Free SAR Ship Detection

Synthetic aperture radar (SAR) images of ships have complex background interference, multi-scale targets, and irregular distribution characteristics. However, existing mainstream SAR ship detection algorithms rely on manually designed hyperparameters. This results in poor robustness, which makes it difficult to effectively balance the detection accuracy and speed. To solve these problems, a novel anchor-free SAR ship detection algorithm based on self-balancing position attention (SBPA) is proposed. First, a lightweight feature extraction backbone (GhostVS-Net) is designed by FOCUS, ghost, and separable convolution modules to extract feature information. This helps to extract the contour features of ships and suppress unrelated interference, making it more suitable for the scattering characteristics of SAR images. Second, an SBPA module is designed, which balances the local image features under multiple receptive fields, and aggregates the global position information and spatial context information. The proposed SBPA module considers the characteristics of background, scale, and distribution of SAR images to obtain greatly improved positioning accuracy. Finally, the feature pyramid network is applied to fuse the scale context information, and further improve the accuracy of detection. Experimental results on the SSDD and HRSID datasets show that the proposed network bears feature of accurate detection and strong robustness, with the mean average precision reaches 99.72% and 95.30%, which reveals that it outperforms all current state-of-the-art algorithms with super high performances

Stability and activity of cellulase modified with polyethylene glycol (PEG) at different amino groups in the ionic liquid [C₂OHmim][OAc]

<p>Polyethylene glycol (PEG), as a suitable tool to improve enzyme stability, such as monomethoxyl-polyethylene glycol aldehyde (mPEG-ALD) and monomethoxyl-polyethylene glycol succinimide (mPEG-SPA), has been appended at the ε-amino group of lysine or the N-terminal α-amino acid residue of commercial cellulase. The modified cellulases thus obtained are designated as Cell-ALD and Cell-SPA, respectively. The stabilities and activities of these modified cellulases have been studied in the ionic liquid [C₂OHmim][OAc]. Cell-ALD showed excellent stability and activity in [C₂OHmim][OAc], such as the activity of Cell-ALD 5k (molecular weight of ALD is 5000), which can reach above 80% of its original value after remaining in [C₂OHmim][OAc] for 24 h, and outstanding performance in the hydrolysis of natural cellulose.</p

Liquid Hydrocarbon Fuels from Catalytic Cracking of Waste Cooking Oils Using Basic Mesoporous Molecular Sieves K₂O/Ba-MCM-41 as Catalysts

Mesoporous molecular sieves K₂O/Ba-MCM-41, which feature base sites, were prepared under hydrothermal conditions. The structure, base properties, and catalytic activity of the mesoporous molecular sieves as heterogeneous catalysts for the cracking of waste cooking oil (WCO) were then studied in detail. K₂O/Ba-MCM-41 exhibited higher catalytic performance for the cracking of WCO than traditional base catalysts such as Na₂CO₃ and K₂CO₃. Moreover, the cracking of WCO generates fuels (main composition is C₁₂∼C₁₇ alkane or olefin) that have similar chemical compositions to diesel-based fuels, and K₂O/Ba-MCM-41 is of excellent stability. The catalyst could be recycled and reused with negligible loss in activity for four cycles. K₂O/Ba-MCM-41 is an environmentally benign heterogeneous basic catalyst for the production of liquid hydrocarbon fuels from low quality feed stocks