Facile synthesis of 3-substituted quinazoline-2,4-dione and 2,3-di-substituted quinazolinone derivatives

3-Substituted quinazoline-2,4(1H,3H)-dione and 2,3-di-substituted quinazolineone derivatives attract considerable interest due to their pharmacological properties. In this paper, we report the synthesis of N-substituted-3-nitrophthalimide derivatives II?CIII, the reactions of phthalimide III with amines, hydrazines, and amino acid derivatives to synthesise a small library of 3-substituted-5-nitroquinazoline-2,4(1H,3H)-diones IV?CXIV and 2,3-di-substituted-6-nitro-quinazolineones XVIII?CXIX.     

Production from ilmenite of TiO2-supported catalysts for selective catalytic reduction of NO with NH3

Economic production of titanium dioxide (yield >98 %) from ilmenite has been achieved by use of a modified sulfate reduction process. A series of samples were prepared by varying the concentration of titanium dioxide nuclei (0.2, 0.3, and 0.6 %) and further impregnation with antimony and vanadia. The structural and acidic properties of the samples were comprehensively studied by X-ray diffraction (XRD), transmission electron microscopy, BJH pore size distribution, and temperature-programmed desorption of NH₃. The XRD results revealed the presence of intense peaks from anatase titanium dioxide. Enhancement of surface area was observed for second-time filtered samples, possibly because of loss of iron from the bulk. As a result, formation of additional micropores was apparent from N₂ adsorption and desorption isotherms. Among all the antimony and vanadia-doped samples, the first-time filtered sample with the low concentration of nuclei (0.2 %) had the highest catalytic activity at low temperatures, owing to its larger pore size and abundant acidic species.     

Determination of Lambda-Cyhalothrin and Malathion Residues in Locust by Gas Chromatography with Electron Capture Detection

Journal of Analytical Chemistry - A reliable, easy, and reproducible method was developed for the quantification of λ-cyhalothrin and malathion residues in desert locust. For the extraction, a...     

Magnetic-field effects on the size of vortices below the surface of NbSe2 detected using low energy beta-NMR

A low energy radioactive beam of polarized 8Li has been used to observe the vortex lattice near the surface of superconducting NbSe2. The inhomogeneous magnetic-field distribution associated with the vortex lattice was measured using depth-resolved beta-detected NMR. Below Tc, one observes the characteristic line shape for a triangular vortex lattice which depends on the magnetic penetration depth and vortex core radius. The size of the vortex core varies strongly with the magnetic field. In particular, in a low field of 10.8 mT, the core radius is much larger than the coherence length. The possible origin of these giant vortices is discussed.     

The semiclassical regime of the chaotic quantum-classical transition

An analysis of the semiclassical regime of the quantum-classical transition is given for open, bounded, one-dimensional chaotic dynamical systems. Environmental fluctuations-characteristic of all realistic dynamical systems-suppress the development of a fine structure in classical phase space and damp nonlocal contributions to the semiclassical Wigner function, which would otherwise invalidate the approximation. This dual regularization of the singular nature of the semiclassical limit is demonstrated by a numerical investigation of the chaotic Duffing oscillator.     

Empirical correlation study of dryout heat transfer at high pressure using high order neural network and feed forward neural network

A hybrid high order neural network (HHONN) and a feed forward neural network (FNN) are developed and applied to find an optimized empirical correlation for prediction of dryout (DO) heat transfer. The values predicted by the HHONN and FNN models are compared with each other and also with the previous values of empirical correlation. HONN successfully provides an efficient open-box model of nonlinear input–output mapping which provides easier understanding of data mining. By removing the hidden layers, HONN structures become simpler than FNNs and initialization of learning parameters (weights) will not be catastrophic. The RMS results show that the HHONN model has superior fitting specification for prediction of DO heat transfer problem compared to the other prediction methods.     

Millimeter-wave enabled PAM-4 data transmission over hybrid FSO-MMPOF link for access networks

Optical Review - In this paper, we propose a full duplex architecture based on a hybrid link composed of free space optics (FSO) and multimode plastic optical fiber (MMPOF) for short-range wireless...     

A Bootstrap Framework for Aggregating within and between Feature Selection Methods

In the past decade, big data has become increasingly prevalent in a large number of applications. As a result, datasets suffering from noise and redundancy issues have necessitated the use of feature selection across multiple domains. However, a common concern in feature selection is that different approaches can give very different results when applied to similar datasets. Aggregating the results of different selection methods helps to resolve this concern and control the diversity of selected feature subsets. In this work, we implemented a general framework for the ensemble of multiple feature selection methods. Based on diversified datasets generated from the original set of observations, we aggregated the importance scores generated by multiple feature selection techniques using two methods: the Within Aggregation Method (WAM), which refers to aggregating importance scores within a single feature selection; and the Between Aggregation Method (BAM), which refers to aggregating importance scores between multiple feature selection methods. We applied the proposed framework on 13 real datasets with diverse performances and characteristics. The experimental evaluation showed that WAM provides an effective tool for determining the best feature selection method for a given dataset. WAM has also shown greater stability than BAM in terms of identifying important features. The computational demands of the two methods appeared to be comparable. The results of this work suggest that by applying both WAM and BAM, practitioners can gain a deeper understanding of the feature selection process.