Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

Owing to massive technological developments in Internet of Things (IoT) and cloud environment, cloud computing (CC) offers a highly flexible heterogeneous resource pool over the network, and clients could exploit various resources on demand. Since IoT-enabled models are restricted to resources and require crisp response, minimum latency, and maximum bandwidth, which are outside the capabilities. CC was handled as a resource-rich solution to aforementioned challenge. As high delay reduces the performance of the IoT enabled cloud platform, efficient utilization of task scheduling (TS) reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user job. Therefore, this article concentration on the design of an oppositional red fox optimization based task scheduling scheme (ORFO-TSS) for IoT enabled cloud environment. The presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud platform. It achieves the makespan by performing optimum TS procedures with various aspects of incoming task. The designing of ORFO-TSS method includes the idea of oppositional based learning (OBL) as to traditional RFO approach in enhancing their efficiency. A wide-ranging experimental analysis was applied on the CloudSim platform. The experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.     

Effect of Silicon Carbide on Microstructural,Mechanical and Corrosion Behavior of Electrolytic Copper Matrix Composite Produced by the Powder Metallurgy Route

Silicon - In this work Copper based composites were synthesized from Cu and SiC powders using Powder Metallurgy (PM) technique. The composition of the composites are Cu, Cu-5 wt% SiC,...     

Direct Observation of Contact Reaction Induced Ion Migration and its Effect on Non-Ideal Charge Transport in Lead Triiodide Perovskite Field-Effect Transistors

The migration of ionic defects and electrochemical reactions with metal electrodes remains one of the most important research challenges for organometal halide perovskite optoelectronic devices. There is still a lack of understanding of how the formation of mobile ionic defects impact charge carrier transport and operational device stability, particularly in perovskite field-effect transistors (FETs), which tend to exhibit anomalous device characteristics. Here, the evolution of the n-type FET characteristics of one of the most widely studied materials, Cs_0.05FA_0.17MA_0.78PbI_3, is investigated during repeated measurement cycles as a function of different metal source–drain contacts and precursor stoichiometry. The channel current increases for high work function metals and decreases for low work function metals when multiple cycles of transfer characteristics are measured. The cycling behavior is also sensitive to the precursor stoichiometry. These metal/stoichiometry-dependent device non-idealities are correlated with the quenching of photoluminescence near the positively biased electrode. Based on elemental analysis using electron microscopy the observations can be understood by an n-type doping effect of metallic ions that are created by an electrochemical interaction at the metal–semiconductor interface and migrate into the channel. The findings improve the understanding of ion migration, contact reactions, and the origin of non-idealities in lead triiodide perovskite FETs.     

Optimization of Parameters to Improve the Properties of AA7178/Si3N4 Composites Employing Taguchi Approach

Silicon - In the recent days, the employ of aluminum alloy has enriched dramatically especially in engineering applications extensively employed in ship building, aerospace, structural,...     

Exploration of radiative heat on the Jeffery fluid flow in a microchannel for the impact of suction/injection

The purpose of the present paper is to investigate the flow and heat transfer of thermal radiation on the Jeffery fluid flow within a microchannel for the effects of the superhydrophobic surface (SHS) within suction/injection. The governing differential equations of motion and heat transfer are transformed into nonlinear coupled ordinary differential equations (ODEs) using appropriate similarity transformations. The ODEs are solved along with boundary conditions by adopting Runge–Kutta with shooting technique. Symbolic computational software such as MATLAB, the solver bvp4c syntax examines the behavior of the relevant physical parameters. However, some effective emerging parameters on the flow problem reveal that the microchannel walls within the suction/injection parameter increase the temperature, and the SHS is heated. In contrast, without slip, the opposite behavior is rendered. It is clearly shown that the velocity profile diminishes with increasing the Prandtl number. Furthermore, it is noticed that velocity decreases for increasing values of Hartmann number. Comparison with available results for particular cases is an excellent agreement.     

Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all around. Hence, the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper yield. In present decade, the application of deep learning models in many fields of research has created greater impact. The increasing soil data availability of soil data there is a greater demand for the remotely avail open source model, leads to the incorporation of deep learning method to predict the soil quality. With that concern, this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning (ISQP-DL). The work considers the chemical, physical and biological factors of soil in particular area to estimate the soil quality. Firstly, pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression (DNNR) model. Secondly, soil nutrient data has been given as second input to the DNNR model. By utilizing this data set, the DNNR method is used to evaluate the fertility rate by which the soil quality has been estimated. For training and testing, the model uses Deep Neural Network Regression (DNNR), by utilizing the dataset. The results show that the proposed model is effective for SQP (Soil Quality Prediction Model) with efficient good fitting and generality is enhanced with input features with higher rate of classification accuracy. The results show that the proposed model achieves 96.7% of accuracy rate compared with existing models.