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.     

Development of high performance amine functionalized zeolitic imidazolate framework (ZIF-8)/cellulose triacetate mixed matrix membranes for CO2/CH4 separation

High cost and complex fabrication process of inorganic membranes and lower position of pristine polymeric membranes in the Robeson upper bound curve urged the researchers to develop mixed matrix membranes (MMMs). Cellulose acetate being most commercially used polymer, dominates the market of CO₂ separation mainly because of low cost and environmental friendly resource. In the present study, MMMs consists of amine functionalized zeolitic imidazolate framework (NH₂-ZIF-8) and cellulose triacetate were fabricated for the first time. NH₂-ZIF-8 was used as a filler because the pore size of ZIF-8 is between the kinetic diameter of separating gases (CO₂ and CH₄). Moreover,  NH₂ group attached on the surface of ZIF-8 has affinity with condensable gases like CO₂. Morphology, crystallinity, tensile strength and functional groups of fabricated membranes were investigated using different analytical techniques. Results revealed that the increase of feed pressure has increased CO₂ permeability and decreased permselectivity. However, improvements in gas separation performance were observed with the addition of nanofiller. Best position in Robeson's upper bound curve at 4 bar was obtained with 10 wt% loading with CO₂ permeability and CO₂/CH₄ permselectivity of 218 barrer and 13.84, respectively. The improvement in the gas separation performance with loading is attributed to the increased diffusion coefficients as well as solubility coefficients, which was increased to 33% and 3.8%, respectively.     

Honeycomb-like bio-based carbon framework decorated with ternary tantalum-based compounds as efficient and durable electrocatalysts for triiodide reduction reaction

Finding an inexpensive and effective clean energy electrocatalyst is highly important for the new generation of photovoltaic devices. Herein, we report a facile and universal in situ co-precipitation strategy to load three novel tantalum-based compounds (NiTa₂O₆, MnTa₂O₆, and AlTaO₄) on honeycomb-like bio-based carbon (HBC) frameworks. The HBC framework with unique honeycomb-like network structure serves as a support material in nanohybrids that can provide rich surface active sites and rapid electron transport channels for triiodide reduction reaction. The adoption of widely available and abundant biomass-derived carbon into tantalum-based compounds markedly boosts electrocatalytic properties of nanohybrids and exhibits robust corrosion resistance because nanohybrids adequately utilize the synergistic effects of different components. The photovoltaic devices fabricated with NiTa₂O₆/HBC, MnTa₂O₆/HBC, and AlTaO₄/HBC counter electrode catalysts demonstrate the brilliant power conversion efficiency (PCE) of 7.09%, 7.39%, and 7.86%, respectively, outperforming the Pt-based cells (6.80%). This work offers a strategy for the further rational design of efficient, inexpensive, and durable electrocatalysts for advanced energy technology applications.     

Developing pressure sensors from impregnated textile sandwiched in inkjet-printed electrodes

Journal of Materials Science: Materials in Electronics - This work presents an interesting fabrication route toward development of pressure sensing patch by utilizing electrically conductive cotton...     

Platinum‐promoted fibrous silica Y zeolite with enhanced mass transfer as a highly selective catalyst for n‐dodecane hydroisomerization

A fibrous silica zeolite Y (HY@KCC‐1) catalyst with a high surface area of 568 m²/g and unique core‐shell morphology was successfully synthesized via a modified KCC‐1 synthesis method. Characterization of the catalysts was achieved with X‐ray powder diffraction (XRD), field emission scanning microscope (FESEM), N₂ adsorption/desorption, and 2,6‐dimethylpyridine adsorbed Fourier‐transform infrared spectroscopy (FTIR). The Pt/HY@KCC‐1 has displayed complete n‐dodecane conversion coupled with an incredibly enhanced isomer yield of 72% at 350°C, nearly two‐fold higher than that of unmodified Pt/HY catalyst. Remarkably, Pt/HY@KCC‐1 had an internal effectiveness factor (η) of unity and negligible internal diffusion limitation, thus suggesting its potential application in hydroisomerization of higher hydrocarbons for enhancing fuel properties.     

Transforming Hand Drawn Wireframes into Front-End Code with Deep Learning

The way towards generating a website front end involves a designer settling on an idea for what kind of layout they want the website to have, then proceeding to plan and implement each aspect one by one until they have converted what they initially laid out into its Html front end form, this process can take a considerable time, especially considering the first draft of the design is traditionally never the final one. This process can take up a large amount of resource real estate, and as we have laid out in this paper, by using a Model consisting of various Neural Networks trained on a custom dataset. It can be automated into assisting designers, allowing them to focus on the other more complicated parts of the system they are designing by quickly generating what would rather be straightforward busywork. Over the past 20 years, the boom in how much the internet is used and the sheer volume of pages on it demands a high level of work and time to create them. For the efficiency of the process, we proposed a multi-model-based architecture on image captioning, consisting of Convolutional neural network (CNN) and Long short-term memory (LSTM) models. Our proposed approach trained on our custom-made database can be automated into assisting designers, allowing them to focus on the other more complicated part of the system. We trained our model in several batches over a custom-made dataset consisting of over 6300 files and were finally able to achieve a Bilingual Evaluation Understudy (BLEU) score for a batch of 50 hand-drawn images at 87.86%