Water pollution is one of the most pervasive problems afflicting people throughout the world, while adsorption is the most widely used method to remove the contaminants from water. Here, in this paper, we report an eco-friendly graphene oxide-chitosan (GO-CS) hydrogel as a new type of adsorbent for water purification. The GO-CS hydrogels were prepared via self-assembly of GO sheets and CS chains. A three-dimensional network composed of GO sheets crosslinked by CS was found in GO-CS hydrogels.The GO-CS composite hydrogels showed high adsorption capacity towards different contaminants, including cationic and anionic dyes, as well as heavy metal ions. The mechanism of the dye adsorption was investigated with a spectral method, and an electrostatic interaction was found to be the major interaction between ionic dyes and the hydrogel. The influence of the hydrogel composition on the adsorption capacity towards different adsorbates was also studied. Finally, it was demonstrated that the GO-CS hydrogel can be used as column packing, to fabricate a column for water purification by filtration.

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Sensor fusion for object tracking has become an active research direction during the past few years. But how to do it in a robust and principled way is still an open problem. In this paper, we propose a new fusion framework that combines both the bottom-up and top-down approaches to probabilistically fuse multiple sensing modalities. At the lower level, individual vision and audio trackers are designed to generate effective proposals for the fuser. At the higher level, the fuser performs reliable tracking by verifying hypotheses over multiple likelihood models from multiple cues. Different from the traditional fusion algorithms, the proposed framework is a closed-loop system where the fuser and trackers coordinate their tracking information. Furthermore, to handle non-stationary situations, the proposed framework evaluates the performance of the individual trackers and dynamically updates their object states. We present a real-time speaker tracking system based on the proposed framework by fusing object contour, color and sound source location. We report robust tracking results.

2D ultrathin aluminum oxide (2D-Al2O3) nanosheets are prepared by duplicating graphene oxide. An amorphous precursor of the hydroxide of aluminum is first deposited onto graphene oxide sheets, which are then converted into 2D-Al2 O3 nanosheets by calcination, while the graphene oxide is removed. The 2D-Al2O3 nanosheets have a large specific surface area and a superior adsorption capacity to fluoride ions.

A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction, and allows the use of pretrained 2D detection models when training data is limited, as is often the case for medical applications. Its integration in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver Tumor Segmentation (LiTS) Challenge, outperforming the previous challenge winner by 3.9 points and achieving top performance on the LiTS leader board at the time of paper submission. Detection experiments on the DeepLesion dataset also show that the addition of VA to existing object detectors enables a 69.1 sensitivity at 0.5 false positive per image, outperforming the best published results by 6.6 points.