Transition-Metal Catalyzed C-H Bond Amination from Aryl Azide

This thesis describes transition metal catalyzed benzylic and aliphatic C–H amination reaction by employing aryl azides as the nitrogen source, with the generation of indoline. Additionly, Rh(II) carboxylate promoting disubtituted indole formation from β, β-disubstituted styryl azide is discussed afterwards

Rh₂(II)-Catalyzed Intramolecular Aliphatic C–H Bond Amination Reactions Using Aryl Azides as the N-Atom Source

Rhodium­(II) dicarboxylate complexes were discovered to catalyze the intramolecular amination of unactivated primary, secondary, or tertiary aliphatic C–H bonds using aryl azides as the N-atom precursor. While a strong electron-withdrawing group on the nitrogen atom is typically required to achieve this reaction, we found that both electron-rich and electron-poor aryl azides are efficient sources for the metal nitrene reactive intermediate

Identification of double-yolked duck egg using computer vision

<div><p>The double-yolked (DY) egg is quite popular in some Asian countries because it is considered as a sign of good luck, however, the double yolk is one of the reasons why these eggs fail to hatch. The usage of automatic methods for identifying DY eggs can increase the efficiency in the poultry industry by decreasing egg loss during incubation or improving sale proceeds. In this study, two methods for DY duck egg identification were developed by using computer vision technology. Transmittance images of DY and single-yolked (SY) duck eggs were acquired by a CCD camera to identify them according to their shape features. The Fisher’s linear discriminant (FLD) model equipped with a set of normalized Fourier descriptors (NFDs) extracted from the acquired images and the convolutional neural network (CNN) model using primary preprocessed images were built to recognize duck egg yolk types. The classification accuracies of the FLD model for SY and DY eggs were 100% and 93.2% respectively, while the classification accuracies of the CNN model for SY and DY eggs were 98% and 98.8% respectively. The CNN-based algorithm took about 0.12 s to recognize one sample image, which was slightly faster than the FLD-based (about 0.20 s). Finally, this work compared two classification methods and provided the better method for DY egg identification.</p></div

Flow chart of DY egg identification algorithms.

<p>(a) The FLD-based algorithm. (b) The CNN-based algorithm.</p

Flow chart of morphological characteristics extraction algorithm.

<p>(a) ROI image. (b) Threshold segmentation. (c) Subtraction operation. (d) Eggshell boundary removal.</p

Typical yolk image with two separate yolk regions.

<p>(a) Original color image. (b) Yolk binary image. (c) Yolk boundary image using the method described by Gonzalez [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190054#pone.0190054.ref030" target="_blank">30</a>].</p

Classification results for duck egg yolk type using FLD.

<p>Classification results for duck egg yolk type using FLD.</p

Reconstruction error versus the number of FDs.

<p>Reconstruction error versus the number of FDs.</p

Two typical binary yolk images.

<p>(a) From SY egg. (b) From DY egg.</p

Evaluation results of multi-classification cross-validation feature selection for cGAN-based network intrusion detection strategy.

Evaluation results of multi-classification cross-validation feature selection for cGAN-based network intrusion detection strategy.</p