Optimal Water Allocation of Surface and Ground Water Resources Under Climate Change with WEAP and IWOA Modeling

Water Resources Management - The water evaluation and planning (WEAP) approach and the invasive weed optimization algorithm (IWOA) are herein employed to determine the optimal operating policies in...     

Evolving an Accurate Decision Tree-Based Model for Predicting Carbon Dioxide Solubility in Polymers

Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time-consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO₂ solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO₂ solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach.     

Using Markov Learning Utilization Model for Resource Allocation in Cloud of Thing Network

Wireless Personal Communications - The integration of the Internet of Things (IoT) and cloud environment has led to the creation of Cloud of Things, which has given rise to new challenges in IoT...     

Novel electrospun polymer electrolytes incorporated with Keggin‐type hetero polyoxometalate fillers as solvent‐free electrolytes for lithium ion batteries

In this study, solvent‐free nanofibrous electrolytes were fabricated through an electrospinning method. Polyethylene oxide (PEO), lithium perchlorate and ethylene carbonate were used as polymer matrix, salt and plasticizer respectively in the electrolyte structures. Keggin‐type hetero polyoxometalate (Cu‐POM@Ru‐rGO, Ni‐POM@Ru‐rGO and Co‐POM@Ru‐rGO (POM, polyoxometalate; rGO, reduced graphene oxide)) nanoparticles were synthesized and inserted into the PEO‐based nanofibrous electrolytes. TEM and SEM analyses were carried out for further evaluation of the synthesized filler structures and the electrospun nanofibre morphologies. The fractions of free ions and crystalline phases of the as‐spun electrolytes were estimated by obtaining Fourier transform infrared and XRD spectra, respectively. The results showed a significant improvement in the ionic conductivity of the nanofibrous electrolytes by increasing filler concentrations. The highest ionic conductivity of 0.28 mS cm^?1 was obtained by the introduction of 0.49 wt% Co‐POM@Ru‐rGO into the electrospun electrolyte at ambient temperature. Compared with solution‐cast polymeric electrolytes, the electrospun electrolytes present superior ionic conductivity. Moreover, the cycle stability of the as‐spun electrolytes was clearly improved by the addition of fillers. Furthermore, the mechanical strength was enhanced with the insertion of 0.07 wt% fillers to the electrospun electrolytes. The results implied that the prepared nanofibres are good candidates as solvent‐free electrolytes for lithium ion batteries. © 2020 Society of Chemical Industry     

F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems

The Journal of Supercomputing - Internet of Things (IoT) is an emerging paradigm that consists of numerous connected and interrelated devices with embedded sensors, exchanging data with each other...     

Characterization of titania thin films grown by dip-coating technique

A dip-coating technique was employed to prepare anatase phase of titania thin films. Fluorine doped tin oxide substrates were used to prepare titania thin films. The samples were annealed at 550 °C for 18 h. X-ray diffraction results revealed the amorphous and anatase phases of TiO₂ for as-synthesized and annealed samples, respectively. The crystallite size of anatase TiO₂ thin films was almost 25 nm for annealed samples. UV–visible confirmed the energy band gap 3.86 and 3.64 eV for as-prepared and calcinated titania thin films. The reduction in the energy band gap could be due to the change in crystallization and agglomeration of small grains after calcination. The morphology of the prepared films was investigated by field emission scanning electron microscopy which demonstrated the agglomeration of spherical particles of TiO₂ with average particle size of about 30 nm. The molecular properties (chemical bonding) of the samples were investigated by means of Fourier Transform Infrared (FTIR) spectroscopy. FTIR analysis exhibited the formation of titania, functional group OH, hydroxyl stretching vibrations of the C–OH groups, bending vibration mode of H–O–H, alkyl C–H stretch, stretching band of Ti–OH, CN asymmetric band stretching, and C=O saturated aldehyde.     

A compact split-step finite difference method for solving the nonlinear Schrödinger equations with constant and variable coefficients

We propose a compact split-step finite difference method to solve the nonlinear Schrödinger equations with constant and variable coefficients. This method improves the accuracy of split-step finite difference method by introducing a compact scheme for discretization of space variable while this improvement does not reduce the stability range and does not increase the computational cost. This method also preserves some conservation laws. Numerical tests are presented to confirm the theoretical results for the new numerical method by using the cubic nonlinear Schrödinger equation with constant and variable coefficients and Gross-Pitaevskii equation.     

A latency-aware task scheduling algorithm for allocating virtual machines in a cost-effective and time-sensitive fog-cloud architecture

The Journal of Supercomputing - Recently, with the expansion of communications and generated data, the need for processing this high volume of data in minimum time and maximum speed has increased....     

Reordering for improving global Arnoldi–Tikhonov method in image restoration problems

This paper discusses the solution of large-scale linear discrete ill-posed problems arising from image restoration problems. Since the scale of the problem is usually very large, the computations with the blurring matrix can be very expensive. In this regard, we consider problems in which the coefficient matrix is the sum of Kronecker products of matrices to benefit the computation. Here, we present an alternative approach based on reordering of the image approximations obtained with the global Arnoldi–Tikhonov method. The ordering of the intensities is such that it makes the image approximation monotonic and thus minimizes the finite differences norm. We present theoretical properties of the method and numerical experiments on image restoration.