MXene Hollow Spheres Supported by a C-Co Exoskeleton Grow MWCNTs for Efficient Microwave Absorption.

High-performance microwave absorption (MA) materials must be studied immediately since electromagnetic pollution has become a problem that cannot be disregarded. A straightforward composite material, comprising hollow MXene spheres loaded with C-Co frameworks, was prepared to develop multiwalled carbon nanotubes (MWCNTs). A high impedance and suitable morphology were guaranteed by the C-Co exoskeleton, the attenuation ability was provided by the MWCNTs endoskeleton, and the material performance was greatly enhanced by the layered core-shell structure. When the thickness was only 2.04 mm, the effective absorption bandwidth was 5.67 GHz, and the minimum reflection loss (RLmin) was - 70.70 dB. At a thickness of 1.861 mm, the sample calcined at 700 °C had a RLmin of - 63.25 dB. All samples performed well with a reduced filler ratio of 15 wt%. This paper provides a method for making lightweight core-shell composite MA materials with magnetoelectric synergy.

Tribological Properties of Groove-Textured Ti-6Al-4V Alloys with Solid Lubricants in Dry Sliding against GCr15 Steel Balls.

A nanosecond laser is used to fabricate groove-patterned textures on the upper surface of Ti-6Al-4V alloys, and then molybdic sulfide solid lubricants are filled into the grooves. The treated titanium alloy is subjected to friction and wear tests. The tribological performances of Ti-6Al-4V alloys are evaluated, and the wearing mechanism is analyzed. The combination of solid lubricants and surface texturing can effectively reduce the frictional coefficient and reduce the adhesion of Ti-6Al-4V materials on the steel balls for friction. The main wearing mechanism is the adhesive wear of the Ti-6Al-4V alloy and the adhesion of Ti-6Al-4V alloy materials on the surface of the steel balls. During the friction process, solid lubricants are squeezed from the grooves and coated at the friction interface to form a solid lubrication layer. This is the important reason why the combination of surface texturing and solid lubricants can improve the friction properties of titanium alloys effectively. The combination of solid lubricants and laser surface texturing provides an effective alternative way to improve the tribological properties of titanium alloy materials.

Reverse Attention Based Residual Network for Salient Object Detection.

Benefiting from the quick development of deep convolutional neural networks, especially fully convolutional neural networks (FCNs), remarkable progresses have been achieved on salient object detection recently. Nevertheless, these FCNs based methods are still challenging to generate high resolution saliency maps, and also not applicable for subsequent applications due to their heavy model weights. In this paper, we propose a compact and efficient deep network with high accuracy for salient object detection. Firstly, we propose two strategies for initial prediction, one is a new designed multi-scale context module, the other is incorporating hand-crafted saliency priors. Secondly, we employ residual learning to refine it progressively by only learning the residual in each side-output, which can be achieved with few convolutional parameters, therefore leads to high compactness and high efficiency. Finally, we further design a novel reverse attention block to guide side-output residual learning in a top-down manner. Specifically, the current predicted salient regions are erased from each side-output feature, thus the missing object parts and details can be efficiently learned from these unerased regions, which results in high resolution and accuracy. Extensive experimental results on seven benchmark datasets demonstrate that the proposed network performs favorably against the state-of-the-art methods, and with advantages in terms of simplicity, efficiency and model size.

Embedding Attention and Residual Network for Accurate Salient Object Detection.

Salient object detection is usually used as a preprocessing step to facilitate a variety of subsequent applications which should take little time cost. With the quick development of deep learning recently, profound progresses have been made to achieve a new state-of-the-art performance. However, the learned features of the existing deep learning-based methods are not accurate enough thus leading to unsatisfactory detection in complex scenes, such as low contrast or very similar between salient object and background region and multiple (small) salient objects with diverse characteristics. In addition, some post-processing techniques are usually needed for refinement, which is time consuming. To address these issues, this paper presents an efficient fully convolutional salient object detection network. Specifically, we first introduce a visual attention mechanism to guide feature learning in side output layers. In detail, attention weight is employed in a top-down manner which can bridge high level semantic information to help shallow layers better locate salient objects and also filter out noisy response in the background region. Second, we propose a residual refinement network to fuse the learned multilevel features gradually. Not to simply add or concatenate them step by step as previous works, we introduce a second-order term into element-wise addition to learn stage-wise residual features for refinement. Such a second-order term not only benefits efficient gradient propagation but also increases network nonlinearity. Extensive experiments on seven standard benchmarks demonstrate that the proposed approach achieves consistently superior performance and performs well on small salient object detection in comparison with the very recent state-of-the-arts, especially in the metric of structure-measure.