Prediction of drug synergy score using ensemble based differential evolution

Prediction of drug synergy score is an ill‐posed problem. It plays an efficient role in the medical field for inhibiting specific cancer agents. An efficient regression‐based machine learning technique has an ability to minimise the drug synergy prediction errors. Therefore, in this study, an efficient machine learning technique for drug synergy prediction technique is designed by using ensemble based differential evolution (DE) for optimising the support vector machine (SVM). Because the tuning of the attributes of SVM kernel regulates the prediction precision. The ensemble based DE employs two trial vector generation techniques and two control attributes settings. The initial generation technique has the best solution and the other is without the best solution. The proposed and existing competitive machine learning techniques are applied to drug synergy data. The extensive analysis demonstrates that the proposed technique outperforms others in terms of accuracy, root mean square error and coefficient of correlation.Inspec keywords: cancer, evolutionary computation, support vector machines, regression analysis, drugs, learning (artificial intelligence), medical computingOther keywords: ensemble based differential evolution, specific cancer agents, efficient regression‐based machine learning technique, drug synergy prediction errors, efficient machine learning technique, drug synergy prediction technique, support vector machine, prediction precision, trial vector generation techniques, initial generation technique, drug synergy data, drug synergy score prediction, medical field, SVM kernel attributes, ensemble based DE, control attribute settings, competitive machine learning techniques, root mean square error     

High‐Performance Microwave‐Derived Multi‐Principal Element Alloy Coatings for Tribological Application

The authors report the development of Al_xCoCrFeNi (x = 0.1 to 3) high entropy alloy (HEA) coatings using a simple and straightforward microwave technique. The microstructure of the developed coatings is composed of a cellular structure and diffused interface with the substrate. The microstructure of the HEA coatings varies as a direct function of Al content. An increase in Al fraction shows structural transformation from FCC to BCC along with the evolution of σ and B₂ as the major secondary phases. The diffusion of Mo from the substrate enhances the mixing entropy and promotes σ‐phase formation. The HEA coatings show significantly high hardness compared to SS316L substrate steel (227 HV) with a maximum value of 726 HV observed for three‐molar composition. The fracture toughness exhibits an inverse correlation with the Al fraction with the highest value of around 49 MPa m^1/2 observed for Al_0.1CoCrFeNi coating. The equimolar coating composition shows lowest erosion rates among all the tested samples due to optimum combination of the mechanical properties. The erosion resistance of the equimolar coating is 2 to 5 times higher than steel substrate and around 1.5 times higher than the non‐equimolar counterparts depending upon the impingement angles.

     

A new formulation for state equation representation for Petri nets

A new algorithm for determination of state equations for Petri nets has been proposed. The proposed algorithm results in state equations similar to the state equations for linear sequential machines. All Petri nets may not be represented in the form of linear sequential machines. The resulting state equations are different from Petri net state equations and include output equations used in control theory literature.     

Effects of hBN and Y2O3 Addition on Mechanical and Tribological Behavior of SiC Ceramic Matrix Composites Prepared by Spark Plasma Sintering

Silicon - The objective of the present work is to study the mechanical and tribological properties of SiC-hBN such as hardness, density, fracture toughness, friction and wear behavior with and...     

Aluminum Yttrium Oxide Metal Matrix Composite Synthesized by Microwave Hybrid Sintering: Processing,Microstructure and Mechanical Response

The current study is focused on the microstructure, phase transition, and mechanical properties of the aluminum yttrium oxide (Al–Y₂O₃) composite material. Microwave hybrid sintering using Y₂O₃ nanoparticles as reinforcement at various (i.e., 0.5, 2, 3.5 and 5) wt% was used. Simultaneous thermal analysis (STA) and X-ray photoelectric spectroscopy (XPS) were used to investigate the chemical interaction between Al and Y₂O₃. This research will aid in gaining a better knowledge of the changes in thermal characteristics and compositional changes that occur throughout the microwave hybrid sintering process. The insight into material properties reveals that intermetallic Al₃Y and Al₂O₃ are generated during the synthesis process, which was substantiated by X-ray diffraction (XRD) and energy dispersive spectroscopy (EDX) analysis. The Al–Y₂O₃ composite material has a well-consolidated microstructure and improved mechanical characteristics. To further understand material behaviour, a robust and non-destructive depth sensing nano-indentation technology was being used. With the addition of 5 wt% Y₂O₃, the microhardness of composite material is enhanced by 1.62 times. Furthermore, with 5 wt% Y₂O₃, the produced composite's nano hardness and elastic modulus augmented by 2.43 and 1.8 times, respectively. It is caused by the presence of intermetallic in the composite material, as well as the prevalence of uniform reinforcement distribution.