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This study aims to reveal the tribological mechanism of synergistic effect between MoDTC and P-containing additives in aluminum-based grease.

The authors prepared a molybdenum dialkyl dithiocarbamate (MoDTC) and revealed the tribological mechanism of synergistic effect between MoDTC and P-containing additives in aluminum-based grease by combining with ZDDP and P-containing and S-free additives.

The MoDTC the authors prepared has good friction-reducing and anti-wear properties in aluminum-based grease and has an obvious synergistic effect with ZDDP. MoDTC and ZDDP have a significant synergistic effect on the tribological properties in aluminum-based grease, mainly because of the formation of phosphates and metaphosphates as well as more MoS₂ in the friction film. P element plays a facilitating role in the chemical conversion of MoDTC to MoS₂.

The experiments of MoDTC with tributyl phosphate and trimethylphenyl phosphate confirm that the P element plays a facilitating role in the chemical conversion of MoDTC into MoS₂.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0410

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.