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...     

TXRF spectrometry in the bioanalytical sciences: A brief review

This work describes the basic principles of Total-reflection X-Ray Fluorescence (TXRF) spectrometry and some of its most outstanding applications for investigating metal traces, mainly in biological and biomedical samples. TXRF is an analytical technique that combines the versatility of elemental analysis by X-ray energy dispersion with a low detection limit in the order of picograms of absolute mass or a few ppb (μg L⁻¹) as concentration. The microanalytical capacity of TXRF allows the study of small sample quantities. The TXRF can perform qualitative and mass ratio analyses of the elements present in a sample of only a few hundred nanograms. These properties are of great importance in the research of metal traces in biological systems, as they allow fast, precise, and accurate characterisation of the elemental fingerprint at trace level. Its successful application in studies of the coordination kinetics of new platinum-based antitumor drugs with DNA, its application in the study profile of the metal in healthy and cancerous human tissues, or even its application in the study of the processes of metal diffusion through cell membranes are just a few examples of TXRF capabilities in the biomedical sciences. This brief review's main objective is to provide an overview of the physical principles and possibilities of TXRF spectrometry. It also highlights some of the most outstanding applications that have been developed so far, mainly in the biological field, so that the reader can evaluate their potential applications.     

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...     

Numerical Simulation of a Multi-Output Radar Orthogonal Waveform Based on a Chaos Optimization Algorithm

Russian Physics Journal - Aiming at problems of low probability of interception and poor anti-jamming performance of the multi-output radar, the numerical simulation based on a chaos optimization...     

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...     

Cost-allocation problems for fuzzy agents in a fixed-tree network

Fuzzy Optimization and Decision Making - Cost-allocation problems in a fixed network are concerned with distributing the costs for use by a group of clients who cooperate in order to reduce such...     

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%.