Synthesis,Molecular Docking,and Hemorheological Activity of New 4-(Thien-2-yl)-3-aminopyridine-2(1H)-one Derivatives

Russian Journal of General Chemistry - On the basis of 4-(thien-2-yl)-3-aminopyridine-2(1H)-one, the corresponding chloroacetamide and condensed 1H-pyrido[2,3-b][1,4]oxazine-2(3H)-one were...     

Separation,Preconcentration, and Determination of Lead,Cadmium, and Iron Using Cloud Point Extraction with Flame Atomic Absorption Spectrometry

Journal of Applied Spectroscopy - The concentration of heavy metals in drinking water is an important standard for water quality evaluation and water pipeline corrosion detection. This research...     

Inherent electrochemical activity of TiO2 (anatase,rutile) enhances the charge capacity of cathodes of lithium-sulfur batteries

Journal of Solid State Electrochemistry - The addition of nanocrystalline titanium dioxide (P90) to a cathode of Li/S cell enhances its voltammetric charge capacity by 19%, from which only a small...     

Dislocation Structure Evolution in Low-Stability States of Solid Solutions of Alloys Under Their Deformation

Russian Physics Journal - The results of a study of the dislocation structure evolution in polycrystals of homogeneous solid solutions in low-stability states in Cu-Mn-based alloys with FCC crystal...     

Alpha-gamma decay studies of $$^{247}$$ Md

The European Physical Journal A - The goal of the present paper is twofold. First, a novel expansion many-body method applicable to superfluid open-shell nuclei, the so-called Bogoliubov in-medium...     

Application of Machine Learning for Tool Condition Monitoring in Turning

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.

     

A characterization of sets in ℝ2 with DC distance function

Czechoslovak Mathematical Journal - We give a complete characterization of closed sets F ? ?2 whose distance function dF:= dist(·, F) is DC (i.e., is the difference of two convex...     

Intermetallic Compounds CsnMk (М = Na,K, Rb,Pt, Au,Hg, Te): Geometrical and Topological Analysis,Cluster Precursors,and Self-Assembly of Crystal Structures

Crystallography Reports - A geometrical and topological analysis has been performed, and self-assembly of the crystal structures of intermetallic compounds CsnMk (М = Na, K, Rb, Pt, Au, Hg,...     

Half-Metallic Ferromagnetism in the Co-Doped CdS Diluted Magnetic Semiconductor

JETP Letters - The magnetic properties of Co-doped CdS has been investigated by ab initio calculations using the GGA + U approximation. The study reveals the ferromagnetic ordering of Co-doped CdS...