Cloud vendor selection for the healthcare industry using a big data-driven decision model with probabilistic linguistic information

Applied Intelligence - The role of cloud services in the data-intensive industry is indispensable. Cision recently reported that the cloud market would grow to 55 billion USD, with an active...     

Design and analysis of a sleep and wake-up CMOS low noise amplifier for 5G applications

Telecommunication Systems - This brief proposes a two-stage cascoded CMOS LNA with common drain envelope detection based power reduction method for the 5G applications of 28 GHz frequency....     

An efficient two phase approach for solving reliability–redundancy allocation problem using artificial bee colony technique

The main goal of the present paper is to present a two phase approach for solving the reliability–redundancy allocation problems (RRAP) with nonlinear resource constraints. In the first phase of the proposed approach, an algorithm based on artificial bee colony (ABC) is developed to solve the allocation problem while in the second phase an improvement of the solution as obtained by this algorithm is made. Four benchmark problems in the reliability–redundancy allocation and two reliability optimization problems have been taken to demonstrate the approach and it is shown by comparison that the solutions by the new proposed approach are better than the solutions available in the literature.     

Structural and thermodynamic consequences of introducing {{alpha}}-aminoisobutyric acid in the S peptide of ribonuclease S

The S protein–S peptide interaction is a model systemto study binding thermodynamics in proteins. We substitutedalanine at position 4 in S peptide by     

Structural investigation and giant dielectric response of CaCu<Subscript>3</Subscript>Ti<Subscript>4</Subscript>O<Subscript>12</Subscript> ceramic by Nd/Zr co-doping for energy storage applications

High performance dielectric materials are highly required for practical application for energy storage technologies. In this work, high-k pristine and modified calcium copper titanate having nominal formula Ca_0.95Nd_0.05Cu₃Ti_4?xZr_xO₁₂ (x?=?0.01, 0.03 & 0.10) were synthesized and characterized for structural and dielectric properties. Single phase formation of the synthesized compositions was confirmed by X-ray diffraction patterns and further analysed using Rietveld refinement technique. Phase purity of the synthesized ceramics was further confirmed by Energy-dispersive X-ray Spectroscopy (EDX) analysis. SEM images demonstrated that grain size of the modified CCTO ceramics was controlled by Zr⁴⁺ ions due to solute drag effect. Impedance spectroscopy was employed to understand the grain, grain boundaries and electrode contribution to the dielectric response. Nyquist plots were fitted with a 2R-CPE model which confirms the non-ideality of the system. Substitution of specific concentration of Nd and Zr improved the dielectric properties of high dielectric permittivity (ε′ ~?16,902) and minimal tanδ (≤?0.10) over a wide frequency range. The giant ε′ of the investigated system was attributed to internal barrier layer capacitance (IBLC) effect and reduced tanδ accredited to enhanced grain boundaries resistance due to substitution of Zr⁴⁺ ions at Ti⁴⁺ site.     

Heat transfer in micropolar boundary layer flow near a stagnation point

Using the micropolar theory, a set of boundary layer equations are obtained for the two dimensional flow of an incompressible micropolar fluid near a stagnation point. The boundary conditions of isothermal and insulated wall are taken with VISCOUS DISSIPATION effects. Numerical results for the temperature distribution has been shown graphically for different values of the material parameters and Prandtl number.     

A machine learning system for identifying transmembrane domains from amino acid sequences

We present our machine learning system, that uses inductive logic programming techniques to learn how to identify transmembrane domains from amino acid sequences. Our system facilitates the use of operators such as ‘contains’, that act on entire sequences, rather than on individual elements of a sequence. The prediction accuracy of our new system is around 93%, and this compares favourably with earlier results. This work was carried out with the support of a research grant from ISIS, Fujitsu Laboratories.     

Hydrostatic Pressure Effect on Ga<Subscript>0.75</Subscript>Cr<Subscript>0.25</Subscript>As DMS: DFT Study

This paper studies the first-principles calculations of the effect of hydrostatic pressure on the structural, electronic and magnetic properties of Ga_0.75Cr_0.25As dilute magnetic semiconductor in zb (B3) phase. High-pressure behaviour of Ga_0.75Cr_0.25As has been investigated between 0 and 100 GPa. The calculations have been performed using DFT as implemented in code SIESTA using LDA + U as an exchange-correlation (XC) potential. The study of band structures shows half-metallic ferromagnetic nature with 100% spin polarization. Under application of external pressure, the valence band and conduction band are shifted from their positions which lead to modification of electronic structure.     

Efficient prediction of drug–drug interaction using deep learning models

A drug–drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug–drug interaction is defined as an ill‐posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug–drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug–drug interaction score efficiently. However, these models suffer from the over‐fitting issue. Therefore, these models are not so‐effective for predicting the drug–drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.Inspec keywords: cancer, learning (artificial intelligence), drugs, recurrent neural nets, convolutional neural nets, drug delivery systemsOther keywords: drug synergy, drug–drug interaction score, drug–drug interaction prediction, deep learning, cancer treatment, machine learning, convolutional mixture density recurrent neural network