Synthesis and Characterization of 5-Substituted 1H-Tetrazoles in the Presence of Nano-TiCl4.SiO2

Nano-TiCl4.SiO2 was found to be an extremely efficient catalyst for the preparation of 5-substituted 1H-tetrazole derivatives. Nano-TiCl4.SiO2 is a solid Lewis-acid was synthesized by the reaction of nano-SiO2 and TiCl4. The structure characterization of this acid was achieved with X-ray diffraction, thermogravimetric analysis and electron microscopy. The synthesis of the catalyst is simple and environmentally benign with a good yield. Furthermore, the catalyst is conveniently recoverable and was reused for at least three times. The antimicrobial activities of the synthetic compounds were also determined by both micro dilution methods as recommended by the Clinical Laboratory Standard Institute, but unfortunately did not exhibit antibacterial activities at the highest concentration (256 μL mL–1). Further studies are still needed to investigate the potential biological activities of these compounds against other diseases.KEYWORDS Nano-TiCl4.SiO2, heterogeneous catalyst, 5-substituted 1H-tetrazoles, antibacterial.PDF and Supp files attache

Mind the Gap: A Study in Global Development through Persistent Homology

The Gapminder project set out to use statistics to dispel simplistic notions about global development. In the same spirit, we use persistent homology, a technique from computational algebraic topology, to explore the relationship between country development and geography. For each country, four indicators, gross domestic product per capita; average life expectancy; infant mortality; and gross national income per capita, were used to quantify the development. Two analyses were performed. The first considers clusters of the countries based on these indicators, and the second uncovers cycles in the data when combined with geographic border structure. Our analysis is a multi-scale approach that reveals similarities and connections among countries at a variety of levels. We discover localized development patterns that are invisible in standard statistical methods

Biosynthesis and Characterization of Silver Nanoparticles by Aspergillus Species

Currently, researchers turn to natural processes such as using biological microorganisms in order to develop reliable and ecofriendly methods for the synthesis of metallic nanoparticles. In this study, we have investigated extracellular biosynthesis of silver nanoparticles using four Aspergillus species including A. fumigatus, A. clavatus, A. niger, and A. flavus. We have also analyzed nitrate reductase activity in the studied species in order to determine the probable role of this enzyme in the biosynthesis of silver nanoparticles. The formation of silver nanoparticles in the cell filtrates was confirmed by the passage of laser light, change in the color of cell filtrates, absorption peak at 430 nm in UV-Vis spectra, and atomic force microscopy (AFM). There was a logical relationship between the efficiencies of studied Aspergillus species in the production of silver nanoparticles and their nitrate reductase activity. A. fumigatus as the most efficient species showed the highest nitrate reductase activity among the studied species while A. flavus exhibited the lowest capacity in the biosynthesis of silver nanoparticles which was in accord with its low nitrate reductase activity. The present study showed that Aspergillus species had potential for the biosynthesis of silver nanoparticles depending on their nitrate reductase activity

Towards Emotion Recognition: A Persistent Entropy Application

Emotion recognition and classification is a very active area of research. In this paper, we present a first approach to emotion classification using persistent entropy and support vector machines. A topology-based model is applied to obtain a single real number from each raw signal. These data are used as input of a support vector machine to classify signals into 8 different emotions (calm, happy, sad, angry, fearful, disgust and surprised)

The persistence landscape and some of its properties

Persistence landscapes map persistence diagrams into a function space, which may often be taken to be a Banach space or even a Hilbert space. In the latter case, it is a feature map and there is an associated kernel. The main advantage of this summary is that it allows one to apply tools from statistics and machine learning. Furthermore, the mapping from persistence diagrams to persistence landscapes is stable and invertible. We introduce a weighted version of the persistence landscape and define a one-parameter family of Poisson-weighted persistence landscape kernels that may be useful for learning. We also demonstrate some additional properties of the persistence landscape. First, the persistence landscape may be viewed as a tropical rational function. Second, in many cases it is possible to exactly reconstruct all of the component persistence diagrams from an average persistence landscape. It follows that the persistence landscape kernel is characteristic for certain generic empirical measures. Finally, the persistence landscape distance may be arbitrarily small compared to the interleaving distance.Comment: 18 pages, to appear in the Proceedings of the 2018 Abel Symposiu