Characterization, interfacial rheology, and storage stability of Pickering emulsions stabilized by complex of whey protein isolate fiber and zein derived from micro-endosperm maize.

In present study, we characterized the formation, interfacial rheology, and storage stability of emulsions stabilized by microendosperm maize-derived zein (M-Zein)/whey protein isolate fiber (WPIF) nanoparticles. Microendosperm maize is a newly developed, oleic acid-rich oilseed resource. Recent research has shown that M-Zein possesses unique hydrophobic properties. Combining it with WPIF may enhance its performance as a stabilizer. Optimization of weight ratios for M-Zein/WPIF composites, guided by particle size analysis, fluorescence spectroscopy, three-phase contact angle (θ), and interfacial rheological analysis, revealed that a 4: 6 mass ratio at pH 7 yielded favorable wettability (θ = 91.2°). Interfacial rheology analysis showed that the combination of WPIF reduced M-Zein's interfacial tension to 7.2 mN/m and 36.7 mN/m at oil-water and air-water interfaces, respectively. The M-Zein/WPIF complex exhibited an elastic protein layer at the oil-water interface. Further investigations into nanoparticle concentration, oil phase volume, and pH revealed that emulsions containing 3 % nanoparticles (w/w), 50 % oil phase volume, and pH 7 showed the best storage stability. This research highlights the development of M-Zein/WPIF composited nanoparticles with superior storage stability and interfacial rheology. Additionally, it introduces a novel application for M-Zein, which elevates the value proposition of microendosperm maize.

UnVELO: Unsupervised Vision-Enhanced LiDAR Odometry with Online Correction.

Due to the complementary characteristics of visual and LiDAR information, these two modalities have been fused to facilitate many vision tasks. However, current studies of learning-based odometries mainly focus on either the visual or LiDAR modality, leaving visual-LiDAR odometries (VLOs) under-explored. This work proposes a new method to implement an unsupervised VLO, which adopts a LiDAR-dominant scheme to fuse the two modalities. We, therefore, refer to it as unsupervised vision-enhanced LiDAR odometry (UnVELO). It converts 3D LiDAR points into a dense vertex map via spherical projection and generates a vertex color map by colorizing each vertex with visual information. Further, a point-to-plane distance-based geometric loss and a photometric-error-based visual loss are, respectively, placed on locally planar regions and cluttered regions. Last, but not least, we designed an online pose-correction module to refine the pose predicted by the trained UnVELO during test time. In contrast to the vision-dominant fusion scheme adopted in most previous VLOs, our LiDAR-dominant method adopts the dense representations for both modalities, which facilitates the visual-LiDAR fusion. Besides, our method uses the accurate LiDAR measurements instead of the predicted noisy dense depth maps, which significantly improves the robustness to illumination variations, as well as the efficiency of the online pose correction. The experiments on the KITTI and DSEC datasets showed that our method outperformed previous two-frame-based learning methods. It was also competitive with hybrid methods that integrate a global optimization on multiple or all frames.