Two new noncentrosymmtric fluoroborates, BaZnBO(3)F and BaMgBO(3)F, have been synthesized and characterized. It is found that BaZnBO(3)F possesses a P6 space group with cell parameters of a = 5.0657(1) A, c = 4.2800(1) A, and Z = 1. The structure is built up by five coordinated trigonal bipyramidal ZnO(3)F(2) polyhedra and triangular BO(3) groups. The ZnO(3)F(2) bipyramid shares its three equatorial oxygen atoms with three separate BO(3) groups to form a ZnO(3)-BO(3) layer, and the layers are linked by the apical fluorine atoms in the third dimension. Owing to its special coordination, ZnO(3)F(2) forces its three neighboring BO(3) groups to arrange into a perfect parallel alignment in the plane to give maximum contribution to the nonlinear optical (NLO) effect. Both calculation and powder second harmonic generation tests show that its effective NLO coefficient is on about the same order as that of LiB(3)O(5). In contrast, in BaMgBO(3)F, which crystallizes in space group Cc with a = 17.614(3) A, b = 30.546(6) A, c = 8.060(2) A, and beta = 90.008(2) degrees , Mg coordinates to four oxygen and two fluorine atoms to form a severely distorted MgO(4)F(2) octahedron. It also uses four equatorial oxygen atoms, sharing with three BO(3) groups to form a MgO(4)-BO(3) layer, and again the layers are linked by the apical F atoms. Every Mg atom links with one edge-sharing and two corner-sharing BO(3) groups, leaving the BO(3) group misaligned and resulting in most of their contributions to NLO effects being canceled.

The wobble nucleoside 5-methylaminomethyl-2-thio-uridine (mnm5s2U) is present in bacterial tRNAs specific for Lys and Glu and 5-carboxymethylaminomethyl-2-thio-uridine (cmnm5s2U) in tRNA specific for Gln. The sulfur of (c)mnm5s2U may be exchanged by selenium (Se)–a reaction catalyzed by the selenophosphate-dependent tRNA 2-selenouridine synthase encoded by the mnmH (ybbB, selU, sufY) gene. The MnmH protein has a rhodanese domain containing one catalytic Cys (C97) and a P-loop domain containing a Walker A motif, which is a potential nucleotide binding site. We have earlier isolated a mutant of Salmonella enterica, serovar Typhimurium with an alteration in the rhodanese domain of the MnmH protein (G67E) mediating the formation of modified nucleosides having a geranyl (ge)-group (C10H17-fragment) attached to the s2 group of mnm5s2U and of cmnm5s2U in tRNA. To further characterize the structural requirements to increase the geranylation activity, we here report the analysis of 39 independently isolated mutants catalyzing the formation of mnm5ges2U. All these mutants have amino acid substitutions in the rhodanese domain demonstrating that this domain is pivotal to increase the geranylation activity. The wild type form of MnmH+ also possesses geranyltransferase activity in vitro although only a small amount of the geranyl derivatives of (c)mnm5s2U is detected in vivo. The selenation activity in vivo has an absolute requirement for the catalytic Cys97 in the rhodanese domain whereas the geranylation activity does not. Clearly, MnmH has two distinct enzymatic activities for which the rhodanese domain is pivotal. An intact Walker motif in the P-loop domain is required for the geranylation activity implying that it is the binding site for geranylpyrophosphate (GePP), which is the donor molecule in vitro in the geranyltransfer reaction. Purified MnmH from wild type and from the MnmH(G67E) mutant have bound tRNA, which is enriched with geranylated tRNA. This in conjunction with earlier published data, suggests that this bound geranylated tRNA may be an intermediate in the selenation of the tRNA.

Abstract:Rolling bearings are key components of rotary machines. To ensure early effective fault diagnosis for bearings, a new rolling bearing fault diagnosis method based on variational mode decomposition (VMD) and an improved kernel extreme learning machine (KELM) is proposed in this paper. A fault signal is decomposed via VMD to obtain the intrinsic mode function (IMF) components, and the approximate entropy (ApEn) of the IMF component containing the main fault information is calculated. An eigenvector is created from the approximate entropy of each component. A bearing diagnosis model is created via a KELM; the KELM parameters are optimized using the particle swarm optimization (PSO) algorithm to obtain a KELM diagnosis model with optimal parameters. Finally, the effectiveness of the diagnosis method proposed in this paper is verified via a fan bearing fault diagnosis test. Under identical conditions, the result is compared with the results obtained using a back propagation (BP) neural network, a conventional extreme learning machine (ELM), and a support vector machine (SVM). The test result shows that the method proposed in this paper is superior to the other three methods in terms of diagnostic accuracy.

Insect pests are known to be a major cause of damage to agricultural crops. This paper proposed a deep learning-based pipeline for localization and counting of agricultural pests in images by self-learning saliency feature maps. Our method integrates a convolutional neural network (CNN) of ZF (Zeiler and Fergus model) and a region proposal network (RPN) with Non-Maximum Suppression (NMS) to remove overlapping detections. First, the convolutional layers in ZF Net, without average pooling layer and fc layers, were used to compute feature maps of images, which can better retain the original pixel information through smaller convolution kernels. Then, several critical parameters of the method were optimized, including the output size, score threshold, NMS threshold, and so on. To demonstrate the practical applications of our method, different feature extraction networks were explored, including AlexNet, ResNet and ZF Net. Finally, the model trained on smaller multi-scale images was tested on original large images. Experimental results showed that our method achieved a precision of 0.93 with a miss rate of 0.10. Moreover, our model achieved a mean Accuracy Precision (mAP) of 0.885.