Dichlorido[(1R,2R)-N-(pyridin-2-yl­methyl)cyclo­hexane-1,2-diamine-κ3 N,N′,N′′]mercury(II)

In the title compound, [HgCl2(C12H19N3)], the HgII ion is coordinated by three N atoms of the (1R,2R)-N-(pyridin-2-ylmeth­yl)cyclo­hexane-1,2-diamine ligand and by a Cl atom in the basal plane, and by a second Cl atom in the apical position, within a distorted square-pyramidal geometry. The coordination of the enanti­opure ligand to the metal atom renders the central N atom chiral with an S configuration, so the complex is enanti­omerically pure and corresponds to the S,R,R diastereoisomer. Mol­ecules are linked via weak N—H⋯Cl hydrogen bonds into a one-dimensional hydrogen-bonding supramolecular chain along the crystallographic b axis

Robust Optimization Design of Bolt-Shotcrete Support Structure in Tunnel

The uncertainty of rock and soil parameters is one of the key problems to limit the stability of tunnel support structure. Based on this, a robust optimization design method is proposed to reduce the sensitivity of support system to the uncertainty of rock and soil parameters. By defining the design parameters, noise factors and system response, a robust design system for bolt-shotcrete support structure is established. The non-dominant solutions of system robustness and support cost consist of the Pareto Front, then an knee point recognition method is designed to further filter all non-dominant solutions and determine the only optimal solution. The robust optimization design of the bolt-shotcrete support structure is carried out with a tunnel as the engineering background. The results show that the method can not only improve the stability and adaptability of the supporting structure, but also reduce the economic cost to the greatest extent, which provides a reference for the optimization design of other geotechnical engineering supporting structures

Molecular docking studies on rocaglamide, a traditional Chinese medicine for periodontitis

Purpose: To undertake an in silico assessment of rocaglamide as a potential drug therapy forperiodontitis (dental arthritis).Method: Lamarckian algorithm-based automated docking approach using AutoDock4.2 tool wasapplied for calculating the best possible binding mode of rocaglamide to IL-23p19 and IL-17, the targets of anti-inflammatory drugs in periodontal disease.Results: The top two interactions of rocaglamide with IL-17 (ΔG = -5.45 and -4.83 kcal/mol) were more spontaneous, and the physical interactions (two hydrogen bonds and one π-πbond) generated in the two IL-17- rocaglamide complexes were higher in number than in IL-23p14-rocaglamide complexes.Conclusion: In silico analysis of rocaglamide, a known antimicrobial and anti-inflammatory agent, is a promising natural candidate for periodontitis therapy, and should be further subjected to in vitro and in vivo anti-periodontitis investigations.Keywords: Periodontitis, Inflammation, Rocaglamide, Molecular docking, Lamarckian algorithm, IL- 23p19, IL-1

Deep Descriptor Transforming for Image Co-Localization

Reusable model design becomes desirable with the rapid expansion of machine learning applications. In this paper, we focus on the reusability of pre-trained deep convolutional models. Specifically, different from treating pre-trained models as feature extractors, we reveal more treasures beneath convolutional layers, i.e., the convolutional activations could act as a detector for the common object in the image co-localization problem. We propose a simple but effective method, named Deep Descriptor Transforming (DDT), for evaluating the correlations of descriptors and then obtaining the category-consistent regions, which can accurately locate the common object in a set of images. Empirical studies validate the effectiveness of the proposed DDT method. On benchmark image co-localization datasets, DDT consistently outperforms existing state-of-the-art methods by a large margin. Moreover, DDT also demonstrates good generalization ability for unseen categories and robustness for dealing with noisy data.Comment: Accepted by IJCAI 201

Adaptive Dynamic Surface Control for Generator Excitation Control System

For the generator excitation control system which is equipped with static var compensator (SVC) and unknown parameters, a novel adaptive dynamic surface control scheme is proposed based on neural network and tracking error transformed function with the following features: (1) the transformation of the excitation generator model to the linear systems is omitted; (2) the prespecified performance of the tracking error can be guaranteed by combining with the tracking error transformed function; (3) the computational burden is greatly reduced by estimating the norm of the weighted vector of neural network instead of the weighted vector itself; therefore, it is more suitable for the real time control; and (4) the explosion of complicity problem inherent in the backstepping control can be eliminated. It is proved that the new scheme can make the system semiglobally uniformly ultimately bounded. Simulation results show the effectiveness of this control scheme