Estimation of fog utility pricing: a bio-inspired optimisation techniques' perspective

ABSTRACT

Due to the limited data storage capacity available to Internet service providers and large-scale enterprises, the concept of resource sharing arises. The services can be given on lease to enterprises through Service Level Agreements. Being the extension of the cloud computing, fog computing architecture brings the resources near end users. In order to get the services on lease, the enterprises are supposed to pay for the resources or services which are being used by them. In this paper, four nature inspired algorithms are analysed in order to determine the efficient management of services or resources so that the cost of resources can be reduced and the billing can be attained through calculation of the utilised resources. Pigeon inspired optimization, enhanced differential evolution, binary bat algorithm and simple human learning optimization are used to evaluate the energy consumed by the edge nodes or cloudlets that in turn can be used for estimating the bill through the Time of Use pricing variable. We evaluate the aforementioned techniques to analyse their performance regarding the bill calculation on the basis of fog servers usage. Simulation results demonstrate that BAT algorithm gives significantly better results than other three algorithms in terms of resource utilisation and bill reduction.     

Effect of fasting condition on multi-substrate carbon oxidation and nitrification system in an upflow packed-bed biofilm reactor

The performance of an upflow packed-bed biofilm reactor has been analyzed under multisubstrate limitation by considering simultaneous carbon oxidation and nitrification reactions. The fasting shock load of inlet methanol concentration has a much more pronounced effect on the response of the system compared to that of inlet NH₄⁺ -nitrogen concentration. The exit concentration of NH₄⁺ -nitrogen drops very close to zero, thereby showing essentially complete nitrification during methanol fasting condition. Further, the concentration profiles of oxygen within the biofilm show significant variation during methanol fasting and nonfasting conditions whereas during NH₄⁺ -nitrogen fasting conditions the oxygen concentration profile in the biofilm remains essentially unaffected.     

Design and Experimental Verification of a Linear Permanent Magnet Generator for a Free-Piston Energy Converter

This paper discusses issues that are pertinent to the design of a linear permanent magnet generator for application in a free-piston energy converter. To achieve the required high power density, high efficiency, and low moving mass, a tubular machine equipped with a modular stator winding and a quasi-Halbach magnetized armature is employed. It is shown that the machine design can be optimized with respect to three key dimensional ratios while satisfying other performance requirements. It is also shown that, when the generator is interfaced to an electrical system via a power electronic converter, both the converter volt-amps rating and the converter loss should be taken into account when optimizing the machine design. The performance of such a tubular generator is demonstrated by measurements on a 10-pole/9-slot prototype machine.     

Synthesis,characterization and thermal dissociation of 2‐butoxyethanol‐blocked diisocyanates and their use in the synthesis of isocyanate‐terminated prepolymers

A series of blocked diisocyanates has been synthesized from toluene diisocyante (TDI), isophorone diisocyanate (IPDI), hexamethylene diisocyanate (HDI), 4,4′‐diphenylmethane diisocyanate (MDI) and 2‐butoxyethanol. The synthesis of blocked diisocyanate adducts was confirmed by Fourier transform infrared, ¹H NMR, electron impact mass spectrometry and nitrogen analysis. Differential scanning calorimetry (DSC), thermal gravimetric analysis (TGA) and carbon dioxide evolution were used to determine the minimum de‐blocking temperatures. De‐blocking temperatures determined by these three techniques were found to be in the order DSC > TGA > CO₂ evolution. The effect of different metal catalysts on thermal de‐blocking reaction of the blocked diisocyanates was studied, using the carbon dioxide evolution method. It was found that iron(III) oxide has the maximum catalytic activity on de‐blocking. The solubility of the blocked diisocyanate adducts was determined in different solvents. The study revealed that at 30 °C blocked IPDI and HDI adducts show better solubility than adducts based on TDI and MDI. Isocyanate‐terminated prepolymers of blocked diisocyanates and hydroxyl‐terminated polybutadiene (HTPB) were prepared. The storage stability and gelation times of the prepolymers were studied. Results showed that all the diisocyanate‐HTPB compositions are stable at 50 °C for more than three months. However, aliphatic diisocyanate‐HTPB compositions require greater gelation time than aromatic diisocyanate‐HTPB compositions at their respective de‐blocking temperatures. Copyright © 2007 Society of Chemical Industry     

Dental Composites with Magnesium Doped Zinc Oxide Nanoparticles Prevent Secondary Caries in the Alloxan-Induced Diabetic Model

Antibacterial restorative materials against caries-causing bacteria are highly preferred among high-risk patients, such as the elderly, and patients with metabolic diseases such as diabetes. This study aimed to enhance the antibacterial potential of resin composite with Magnesium-doped Zinc oxide (Mg-doped ZnO) nanoparticles (NPs) and to look for their effectiveness in the alloxan-induced diabetic model. Hexagonal Mg-doped ZnO NPs (22.3 nm diameter) were synthesized by co-precipitation method and characterized through ultraviolet-visible (UV-Vis), Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS) analysis. The Mg-doped ZnO NPs (1, 2.5 and 5% w/w) were then evaluated for antibacterial activity using a closed system in vitro biofilm model. Significant enhancement in the antibacterial properties was observed in composites with 1% Mg-doped ZnO compared to composites with bare ZnO reinforced NPs (Streptococcus mutans, p = 0.0005; Enterococcus faecalis, p = 0.0074, Saliva microcosm, p < 0.0001; Diabetic Saliva microcosm, p < 0.0001). At 1–2.5% Mg-doped ZnO NPs concentration, compressive strength and biocompatibility of composites were not affected. The pH buffering effect was also achieved at these concentrations, hence not allowing optimal conditions for the anaerobic bacteria to grow. Furthermore, composites with Mg-doped ZnO prevented secondary caries formation in the secondary caries model of alloxan-induced diabetes. Therefore, Mg-doped ZnO NPs are highly recommended as an antibacterial agent for resin composites to avoid biofilm and subsequent secondary caries formation in high-risk patients.     

Dual substrate limitations in upflow packed-bed biofilm reactors — a theoretical study

The performance of an upflow packed-bed biofilm reactor has been analyzed under dual substrate limitation conditions. The numerical solution of the proposed equations defining the system has been obtained for a wide range of operating conditions for a case of practical significance involving glucose and oxygen as dual substrates. The results show that the inlet glucose concentration defines the limiting substrate at a position near the inlet of the reactor. For inlet glucose concentrations up to 300 mg/l, glucose acts as the limiting substrate. However, for inlet concentrations of 400 mg/l of glucose or higher, oxygen assumes the role of the limiting substrate at that position. For all other positions in the reactor, glucose acts as the limiting substrate, irrespective of its inlet concentration. Extensive computations were performed in order to define regions where glucose, oxygen or both are limiting. The predicted results have been found to be in agreement with the theoretical criteria, proposed in the literature, of determining the limiting substrate.     

Comparison of artificial neural network and adaptive neuro-fuzzy inference system for predicting the wrinkle recovery of woven fabrics

The aim of this study was to compare the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models for predicting the wrinkle recovery of polyester/cotton woven fabrics. The prediction models were developed using experimental data-set of 115 fabric samples of different constructions. Warp and weft yarn linear densities, ends/25 mm and picks/25 mm, were used as input/predictor variables, and warp and weft crease recovery angles (CRA) as output/response variables. It was found that the prediction accuracy of the ANN models was slightly better as compared with that of ANFIS models developed in this study. However, the ANFIS models could characterize the relationships between the input and output variables through surface plots, which the ANN models could not. The developed models may be used to optimize the fabric construction parameters for maximizing the wrinkle recovery of polyester/cotton woven fabrics.