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 novel image encryption/decryption scheme based on chaotic neural networks

This paper presents a novel image encryption/decryption algorithm based on chaotic neural network (CNN). The employed CNN is comprised of two 3-neuron layers called chaotic neuron layer (CNL) and permutation neuron layer (PNL). The values of three RGB (Red, Green and Blue) color components of image constitute inputs of the CNN and three encoded streams are the network outputs. CNL is a chaotic layer where, three well-known chaotic systems i.e. Chua, Lorenz and Lü systems participate in generating weights and biases matrices of this layer corresponding to each pixel RGB features. Besides, a chaotic tent map is employed as the activation function of this layer, and makes the relationship between the plain image and cipher image nonlinear. The output of CNL, i.e. the diffused information, is the input of PNL, where three-dimensional permutation is applied to the diffused information. The overall process is repeated several times to make the encryption process more robust and complex. A 160-bit-long authentication code has been used to generate the initial conditions and the parameters of the CNL and PNL. Some security analysis are given to demonstrate that the key space of the new algorithm is large enough to make brute-force attacks infeasible and simulations have been carried out with detailed numerical analysis, demonstrating the high security of the new image encryption scheme.     

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....     

A comprehensive review of solar‐driven desalination technologies for off‐grid greenhouses

This paper is motivated by the crisis of freshwater in remote areas around the world and responds to the growing need for sustainable food production in arid lands. It focuses on utilizing solar energy to yield freshwater from the sea or brackish water with less environmental impacts, for greenhouses, which can produce sustainable food all over the year. The integration of various solar‐driven desalinations such as solar still, humidification‐dehumidification, reverse osmosis, electrodialysis, and multieffect and multistage flash with greenhouses are evaluated, for better sustainability towards greenization. The paper first discusses the specifications of solar‐driven desalinations and compares their advantages and limitations. Then, different types of greenhouses are introduced, and their total water requirement is discussed based on their locations, crop type, greenhouse technology, irrigation type, and environmental conditions, as well as their cooling and heating strategies. Later, the existing integration of solar‐driven desalinations with greenhouses are reviewed, and their advantages and limitations are deliberated. Finally, the paper discusses the criteria to be considered when selecting solar‐driven desalinations for greenhouses and presents a detailed comparison between the water production rate and cost as well as the energy consumption of these systems. In the end, the most appropriate combinations of solar‐driven desalinations with greenhouses are recommended based on their water requirement and production cost.