A Simple Nickel Catalyst Enabling an E‐Selective Alkyne Semihydrogenation

Stereoselective alkyne semihydrogenations are attractive approaches to alkenes, which are key building blocks for synthesis. With regards to the most atom economic reducing agent dihydrogen (H 2 ), only few catalysts for the challenging E ‐selective alkyne semihydrogenation have been disclosed, each with a unique substrate scope profile. Here, we show that a commercially available nickel catalyst facilitates the E ‐selective alkyne semihydrogenation of a wide variety of substituted internal alkynes. This results in a simple and broadly applicable overall protocol to stereoselectively access E ‐alkenes employing H 2 which could serve as a general method for synthesis.DFG, 352364740, Diwasserstoff-vermittelte nachhaltige BindungsknüpfungsreaktionenTU Berlin, Open-Access-Mittel - 201

The dispersion of Rayleigh waves in orthotropic layered half-space using matrix method

In this paper, the secular equation of Rayleigh surface waves propagating in an orthotropic layered half-space is derived by the matrix method.  All the layers and the half-space are assumed to have identical principle axes. The explicit form of the matrizant for each layer is obtained by the Sylvester's theorem. The derived secular equation takes only real values and depends only on the dimensionless variables and dimensionless material parameters. Hence, it is convenient in numerical calculation

Environment and Other Problems in Construction Sector - Case of Vietnam Industrial Zones

The purpose of this paper is to address ENVIRONMENT AND OTHER PROBLEMS IN CONSTRUCTION SECTOR - CASE OF VIETNAM INDUSTRIAL ZONES. In this paper, we suggest that Vietnam cities should have policies to encourage and give priority support to production and business establishments that apply clean and environmentally friendly technologies such as gas technology instead of coal technology, firewood in ceramic production, and charcoal production. Bees make use of the residues of buckwheat. High technology is a progressive and inevitable trend to solve environmental pollution in craft villages and industrial zones

DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN

Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets. Previous work primarily focused on data augmentation for semi-supervised and supervised tasks. In this paper, we instead focus on unsupervised anomaly detection and propose a novel generative data augmentation framework optimized for this task. In particular, we propose to oversample infrequent normal samples - normal samples that occur with small probability, e.g., rare normal events. We show that these samples are responsible for false positives in anomaly detection. However, oversampling of infrequent normal samples is challenging for real-world high-dimensional data with multimodal distributions. To address this challenge, we propose to use a GAN variant known as the adversarial autoencoder (AAE) to transform the high-dimensional multimodal data distributions into low-dimensional unimodal latent distributions with well-defined tail probability. Then, we systematically oversample at the `edge' of the latent distributions to increase the density of infrequent normal samples. We show that our oversampling pipeline is a unified one: it is generally applicable to datasets with different complex data distributions. To the best of our knowledge, our method is the first data augmentation technique focused on improving performance in unsupervised anomaly detection. We validate our method by demonstrating consistent improvements across several real-world datasets.Comment: Published as a conference paper at ICDM 2018 (IEEE International Conference on Data Mining