Curved water flow characteristics and its influence on navigation

The ship movement is mainly affected by the circulation current in curve channel. In this paper, the curve circulation is taken as the research object, 3D model is established and scientific numerical simulation is carried out. In order to study and analyze the difference, three curve models with different bending degrees are established in this simulation. Finally, according to the simulation results, the measures for safe navigation are proposed

Click Metallodendrimers And Their Functions

Click chemistry involving copper-catalyzed azide-alkyne cycloaddition (CuAAC) is one of the most useful and powerful methods to construct metallodendrimers. The design of such strategies includes the choice of the copper(I) catalyst source which is critical in the case of the click synthesis of dendrimers. The 1,2,3-triazolyl-containing dendrimers that are produced provide useful intradendritic ligands that are active in supramolecular recognition and catalysis. This account dedicated to K. Peter C. Vollhardt summarizes work conducted mainly in the authors' Bordeaux laboratory. 1 Introduction 2 Click Copper Catalyzed Azide-Alkyne Cycloaddition Metallodendrimer Constructions 3 The Copper-Catalyzed Azide-Alkyne Cycloaddition Reactions in Dendrimers: Mind the Copper(I) Catalyst 4 Redox Recognition with Triazolylferrocenyl- and Triazolylbiferrocenyl-Terminated Dendrimers 5 Click Catalysis with Extremely Efficient Intradendritic Triazolyl-Copper(I) Complexes 6 Click Dendrimers as Stabilizers for Very Efficient Nanoparticle Catalysis of Cross-Coupling Carbon-Carbon Bond Formation and Redox Reactions 7 Click Dendrons and Dendrimers for Efficient Catalysis Using Magnetically Recoverable Catalysts 8 Conclusion and Prospects26111437144

Rationally designed α-conotoxin analogues maintained analgesia activity and weakened side effects

A lack of specificity is restricting the further application of conotoxin from Conus bullatus (BuIA). In this study, an analogue library of BuIA was established and virtual screening was used, which identified high α7 nicotinic acetylcholine receptor (nAChR)-selectivity analogues. The analogues were synthesized and tested for their affinity to functional human α7 nAChR and for the regulation of intracellular calcium ion capacity in neurons. Immunofluorescence, flow cytometry, and patch clamp results showed that the analogues maintained their capacity for calcium regulation. The results of the hot-plate model and paclitaxel-induced peripheral neuropathy model indicated that, when compared with natural BuIA, the analgesia activities of the analogues in different models were maintained. To analyze the adverse effects and toxicity of BuIA and its analogues, the tail suspension test, forced swimming test, and open field test were used. The results showed that the safety and toxicity of the analogues were significantly better than BuIA. The analogues of BuIA with an appropriate and rational mutation showed high selectivity and maintained the regulation of Ca2+ capacity in neurons and activities of analgesia, whereas the analogues demonstrated that the adverse effects of natural α-conotoxins could be reduced

Two-stage deep regression enhanced depth estimation from a single RGB image

Depth estimation plays a significant role in industrial applications, e.g. augmented reality, robotic mapping and autonomous driving. Traditional approaches for capturing depth, such as laser or depth sensor based methods, are difficult to use in most scenarios due to the limitations of high system cost and limited operational conditions. As an inexpensive and convenient approach, using the computational models to estimate depth from a single RGB image offers a preferable way for the depth prediction. Although the design of computational models to estimate the depth map has been widely investigated, the majority of models suffers from low prediction accuracy due to the sole utilization of a one-stage regression strategy. Inspired by both theoretical and practical success of two-stage regression, we propose a two-stage deep regression model, which is composed of two state-of-the-art network architectures, i.e. the fully convolutional residual network (FCRN) and the conditional generation adversarial network (cGAN). FCRN has been proved to possess a strong prediction ability for depth prediction, but fine details in the depth map are still incomplete. Accordingly, we have improved the existing cGAN model to refine the FCRN-based depth prediction. The experimental results show that the proposed two-stage deep regression model outperforms existing state-of-the-art methods