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%.

     

Development of peptidomimetic hydroxamates as PfA-M1 and PfA-M17 dual inhibitors: Biological evaluation and structural characterization by cocrystallization

Plasmodium parasites causing malaria have developed resistance to most of the antimalarials in use, including the artemisinin-based combinations, which are the last line of defense against malaria. This necessitates the discovery of new targets and the development of novel antimalarials. Plasmodium falciparum alanyl aminopeptidase（PfA-M1） and leucyl aminopeptidase（PfA-M17） belong to the M1 and M17 family of metalloproteases respectively and play critical roles in the asexual erythrocytic stage o...     

Supramolecular Arrangement and DFT analysis of Zinc(II) Schiff Bases: An Insight towards the Influence of Compartmental Ligands on Binding Interaction with Protein

We report, for the first time, a detailed crystallographic study of the supramolecular arrangement for a set of zinc(II) Schiff base complexes containing the ligand 2,6-bis((E)-((2-(dimethylamino)ethyl)imino)methyl)-4-R-phenol], where R=methyl/tert-butyl/chloro. The supramolecular study acts as a pre-screening tool for selecting the compartmental ligand R of the Schiff base for effective binding with a targeted protein, bovine serum albumin (BSA). The most stable hexagonal arrangement of the complex [Zn − Me] (R=Me) stabilises the ligand with the highest FMO energy gap (ΔE=4.22 eV) and lowest number of conformations during binding with BSA. In contrast, formation of unstable 3D columnar vertebra for [Zn − Cl] (R=Cl) tend to activate the system with lowest FMO gap (3.75 eV) with highest spontaneity factor in molecular docking. Molecular docking analyses reported in terms of 2D LigPlot+ identified site A, a cleft of domains IB, IIIA and IIIB, as the most probable protein binding site of BSA. Arg144, Glu424, Ser428, Ile455 and Lys114 form the most probable interactions irrespective of the type of compartmental ligands R of the Schiff base whereas Arg185, Glu519, His145, Ile522 act as the differentiating residues with ΔG=−7.3 kcal mol⁻¹.     

Application of advanced nuclear analytical techniques for the electrocatalyst's characterization: Paving the path for mechanistic investigations

As the application of electrocatalyst continues to expand, envisaging the hidden mechanisms occurring at various length scale affecting the catalytic efficiency became important. To enhance the stability of electrocatalyst and reduce the cost, it is of paramount importance to reveal the active site's dynamics (using in situ techniques for getting the real-time information) which directly affect the reactions such as oxygen evolution reaction, hydrogen evolution reaction, and so on. Since such reactions are crucial for many engineering and scientific applications, in situ characterization techniques are required, which could capture such reactions happening at a different length and time scale. This article analyzes the recent progress made in the field of electrocatalyst's characterization using in situ neutron techniques. The article also paves the future path and has delineated the future challenges involved in multiscale correlative techniques (e.g., neutron techniques in the combination of synchrotron or microscopic techniques) used for getting the multiscale (atomic to micrometer range) mechanistic information about the electrocatalyst's working and degradation.     

Activation energy process in hybrid CNTs and induced magnetic slip flow with heat source/sink

Effect of induced magnetic field is critical as a result of much controlled and focused on liquid flow is wanted in numerous modern and clinical procedures for example electromagnetic casting, drug delivery and cooling of nuclear reactors. Hence this investigation explains the behaviour of hybrid carbon nanotubes (CNTs) flow through slipped surface with induced magnetic field. Accumulation of SWCNTs (single wall) and MWCNTs (multi wall) nanomaterial with water base liquid is considered. Thermal performance is analyzed with regular heat source/sink effect. Chemical reaction and activation energy impacts are incorporated in mass equation. Solution of the similarity equations are obtained by adopting RKF45 method. Influence of flow variables are illustrated through graphs and computational values of drag force, Nusselt number and Sherwood number are presented in tables. It is noted that activation energy enhance the concentration field whereas opposite behaviour for reaction rate. Also induce magnetic field boosted with the larger values of magnetic Prandtl number. Furthermore it is observed that hybrid CNTs nanomaterial having higher rate of heating/cooling compare to singular CNTs nanomaterial.     

Stability analysis of fractional order fuzzy cellular neural networks with leakage delay and time varying delays

In this paper we investigated the stability of fractional order fuzzy cellular neural networks with leakage delay and time varying delays. Based on Lyapunov theory and applying bounded techniques of fractional calculation, sufficient criterion are established to guarantee the stability. Hybrid feedback control is applied to derive the proposed results. Finally, numerical examples with simulation results are given to illustrate the effectiveness of the proposed method.