Multi-analyte HPLC–DAD Method for Concurrent Analysis of Six Antimicrobials and Three Proton Pump Inhibitors Frequently used in Management of Helicobacter pylori Infection: Application to Simulated Intestinal Fluid Samples

Chromatographia - The present work deals with the optimization, validation and application of a versatile HPLC–DAD method for concurrent estimation of nine antimicrobials and proton pump...     

Nanoporous NiO@SiO2 photo-catalyst prepared by ion-exchange method for fast elimination of reactive dyes from wastewater

Photo-catalytic elimination of organic contaminants plays a significant role in wastewater treatment. Developing a highly efficient photo-catalyst is one of the leading research topic. Herein, we reported the fabrication of a novel nanoporous NiO@SiO₂ photo-catalyst by a simple ion-exchange method to eliminate the reactive dyes. The synthesized NiO@SiO₂ catalyst exhibited fast photo-degradation and excellent adsorption capability and could efficiently remove Red FN-3GL dye from wastewater, due to a high loading of NiO and a large specific surface area, abundant electron-withdrawing groups, as well as narrow bandgap energy. In addition, the NiO@SiO₂ photo-catalyst also displayed a high capability to remove reactive dyes over a wide range of pH values (pH 3–9). The prominent adsorption and photo-degradation of dyes were strongly dependent on the surface charge of the catalyst and the generation of hydroxyl radicals (OH^?) by the catalyst, respectively. Furthermore, the NiO@SiO₂ photo-catalyst also exhibited excellent recyclability, thus demonstrating the feasibility of practical applications in industries. The strategy of covering the metal oxide to nanoporous silica is a promising method for developing active photo-catalysts and applying them in the wastewater treatments.     

Structural,Computational and 3D Interaction Energy Calculations of the Compound 2-chloro-3-(1-napthyl)-5,5-dimethyl-2-cyclohexenone

Crystallography Reports - The molecular structure of 2-chloro-3-(1'-napthyl)-5,5-dimethyl-2- cyclohexenone was determined by single crystal X-ray diffraction. The compound crystallized in a...     

Finite-sample properties of estimators for first and second order autoregressive processes

Statistical Inference for Stochastic Processes - The class of autoregressive (AR) processes is extensively used to model temporal dependence in observed time series. Such models are easily...     

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%.

     

Viscoelastic-mapping of cellulose nanofibrils using low-total-force contact resonance force microscopy (LTF-CRFM)

Cellulose - Low-total-force contact resonance force microscopy (LTF-CRFM), an atomic force microscopy method, is introduced as a non-destructive means to quantify the local viscoelastic loss...     

Estimation of optimal parameters yielding target amplitude–frequency response in nonlinear vibrating systems by a generalised cluster-based algorithm

Nonlinear Dynamics - Optimisation procedures for nonlinear vibrating systems are made cumbersome due to the fact that the regimes of periodic response are not known beforehand. Moreover, such...     

Correction to: Multivariate cluster weighted models using skewed distributions

Advances in Data Analysis and Classification - In the original publication of the article, the line after equation (5) has been published incorrectly     

Numerical Simulation of the Yield of α Particles and Neutrons from the 11B(p, 3α) and 11B(p,n)11C Nuclear Reactions Induced by Intense Picosecond Laser Radiation

Journal of Experimental and Theoretical Physics - The yields of alpha particles and neutrons from the promising 11B(p, 3α) fusion reaction and the reference 11B(p, n)11C nuclear reaction,...