Maximizing quality of experience through context‐aware mobile application scheduling in cloudlet infrastructure

Application software execution requests, from mobile devices to cloud service providers, are often heterogeneous in terms of device, network, and application runtime contexts. These heterogeneous contexts include the remaining battery level of a mobile device, network signal strength it receives and quality‐of‐service (QoS) requirement of an application software submitted from that device. Scheduling such application software execution requests (from many mobile devices) on competent virtual machines to enhance user quality of experience (QoE) is a multi‐constrained optimization problem. However, existing solutions in the literature either address utility maximization problem for service providers or optimize the application QoS levels, bypassing device‐level and network‐level contextual information. In this paper, a multi‐objective nonlinear programming solution to the context‐aware application software scheduling problem has been developed, namely, QoE and context‐aware scheduling (QCASH) method, which minimizes the application execution times (i.e., maximizes the QoE) and maximizes the application execution success rate. To the best of our knowledge, QCASH is the first work in this domain that inscribes the optimal scheduling problem for mobile application software execution requests with three‐dimensional context parameters. In QCASH, the context priority of each application is measured by applying min–max normalization and multiple linear regression models on three context parameters—battery level, network signal strength, and application QoS. Experimental results, found from simulation runs on CloudSim toolkit, demonstrate that the QCASH outperforms the state‐of‐the‐art works well across the success rate, waiting time, and QoE. Copyright © 2016 John Wiley & Sons, Ltd.     

High surface area and mesoporous activated carbon from KOH-activated dragon fruit peels for methylene blue dye adsorption:Optimization and mechanism study

In tnis study,an alternative precursor for production of activated carbon was introduced using dragon fruit(Hylocereus costaricensis) peel(DFP).Moreover,KOH was used as a chemical activator in the thermal carbonization process to convert DFP into activated carbon(DFPAC).In order to accomplish this research,several approaches were employed to examine the elemental composition,surface properties,amorphous and crystalline nature,essential active group,and surface morphology of the DFPAC.The Brunauer-Emmett-Teller test demonstrated a mesoporous structure of the DFPAC has a high surface area of 756.3 m~2·g~(-1).The cationic dye Methylene Blue(MB) was used as a probe to assess the efficiency of DFPAC towards the removal of MB dye from aqueous solution.The effects of adsorption input factors(e.g.DFPAC dose(A:0.04-0.12 g·L~(-1)), pH(B:3-10),and temperature(C:30-50℃)) were investigated and optimized using statistical analysis(i.e.Box-Behnken design(BBD)).The adsorption kinetic model can be best categorized as the pseudo-first order(PFO).Whereas,the adsorption isotherm model can be best described by Langmuir model,with maximum adsorption capacity of DFPAC for MB dye was 195.2 mg·g~(-1) at 50℃.The adsorption mechanism of MB by DFPAC surface was attributed to the electrostatic interaction,π-π interaction,and H-bonding.Finally,the results support the ability of DFP to be a promising precursor for production of highly porous activated carbon suitable for removal of cationic dyes(e.g.MB).     

Electro-magneto-hydrodynamic flow and radiative heat transfer of the non-Newtonian fluids through a porous micro-channel

Mechanics of Time-Dependent Materials - This problem deals with steady driven pressure flow and heat transfer of electromagneto-hydrodynamic micro-pump of third-grade fluids between two...     

An end-to-end deep learning model for human activity recognition from highly sparse body sensor data in Internet of Medical Things environment

The Journal of Supercomputing - Recognizing human activity from highly sparse body sensor data is becoming an important problem in Internet of Medical Things (IoMT) industry. As IoMT currently uses...     

Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components

Intrusion detection is very serious issue in these days because the prevention of intrusions depends on detection. Therefore, accurate detection of intrusion is very essential to secure information in computer and network systems of any organization such as private, public, and government. Several intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. This issue of the existing techniques is the focus of research in this paper. The poor performance of such techniques is due to raw dataset which confuse the classifier and results inaccurate detection due to redundant features. The recent approaches used principal component analysis (PCA) for feature subset selection which is based on highest eigenvalues, but the features corresponding to the highest eigenvalues may not have the optimal sensitivity for the classifier due to ignoring many sensitive features. Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, this research applied a genetic algorithm to search the genetic principal components that offers a subset of features with optimal sensitivity and the highest discriminatory power. The support vector machine (SVM) is used for classification purpose. This research work used the knowledge discovery and data mining cup dataset for experimentation. The performance of this approach was analyzed and compared with existing approaches. The results show that proposed method enhances SVM performance in intrusion detection that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.     

A survey of mmWave user association mechanisms and spectrum sharing approaches: an overview,open issues and challenges,future research trends

Fifth generation (5G) cellular networks promise to support multi-radio access technologies (multi-RATs) with low and high frequencies aiming at delivering good coverage, several gigabits data rate, and ultra-reliable services. In this context, user-association and resource allocation appear to be a huge challenge due to the variety of specifications and varied propagation environments. In this treatise, the focus is on the technical and administrative difficulties of the adoption of user association (UA) mechanism and spectrum sharing approach (SSA) in millimeter wave (mmWave) systems, for example, the technical design considerations and their underlying options, as well as their impact on users and network performance. In addition, details on the importance of the rules and regulations of SSA are presented. This study also identified a few possible design solutions and potential promising technologies in both UA and SSA. In the context of UA, several mechanisms are identified, such as backhaul-, caching-, and hybrid multi-criteria-aware UA to achieve seamless connectivity and to enhance the network utility. In the context of SSA, this study pinpoints varied subjects that need to be explored, such as joint efficient rules and regulations enactment, assessment of fairness and independence in multi-independent mobile network operators (multi-IMNOs) that support SSA, as well as the implementation of hybrid-SSA via Virtualized Cloud Radio Access Network. Finally, attention is drawn to several key conclusions to enable readers and interested researchers to learn about the most controversial points of mmWave 5G cellular networks.

     

Effect of pressure on the reaction between 3-methyl-1-p-tolyl-triazene and benzoic acid; A kinetic study

Rates of reaction between 3-methyl-1p-tolytriazene and benzoic acid were measured with variation of pressure in chloroform and acetonitrile. Activation volumes were found to be–15 and–4 cm³ mol⁻¹, respectively. The reaction mechanism is discussed in the context of these values.     

Adhesion of cast metal alloy and lithium disilicate copings luted to different core build-up materials with self-adhesive resin cement

This study evaluated the shear bond strength of two coping materials (non-nickel chrome-based cast alloy and lithium disilicate ceramic (IPS Empress) to four different core foundation materials (resin composite, cast metal alloy, lithium disilicate, and dentin), luted with adhesive resin cement (RelyX Unicem). Specimens (N = 56) were fabricated and divided into eight groups (n = 7 per group). Each coping material was luted with self-adhesive resin cement (RelyX Unicem) to the core materials. Bond strength was measured in a Universal Testing Machine (0.5 mm/min). Data were statistically analyzed using a two-way analysis of variance (ANOVA) and Tukey’s HSD tests (alpha = 0.05). Both core (p = 0.000) and coping material type (p = 0.000) significantly affected the mean bond strength (MPa) values. Interaction terms were also significant (p = 0.001). The highest bond strength results were obtained when lithium disilicate was bonded to lithium disilicate (21.48) with the resin cement tested. Lithium disilicate in general presented the highest bond results when bonded to all core materials tested (16.55–21.38) except dentin (3.56). Both cast alloy (2.9) and lithium disilicate (3.56) presented the lowest bond results on dentin followed by cast-alloy-cast alloy combination (3.82).     

Preparation of luminescent polyethylene plastic composite nano-reinforced with glass fibers

Electrospun glass nanofibers (EGNFs) were prepared to reinforce polyethylene (PE) plastic waste towards the development of photochromic anti-counterfeiting patterns and long-persistent photoluminescent materials, such as smart windows and concrete. By physical integration of lanthanide-doped aluminate (LdA) nanoparticles (NPs) into polyethylene plastic reinforced with EGNFs, a transparent lanthanide-doped aluminate nanoparticles (LdANPs)/EGNFs@PE sheet was produced. The colorless EGNFs@PE hybrids became green under ultraviolet (UV) rays and greenish-yellow in a darkened room as proved by CIE Lab and photoluminescence analysis. In the luminescent LdANPs/EGNFs@PE hybrids, the identified photochromism was promptly reversed at low concentrations of LdANPs to designate fluorescence emission. Photoluminescence was maintained with slow reversibility for the high phosphor concentrations to designate afterglow emission. LdANPs exhibit diameters of 5–12 nm, whereas glass nanofibers have diameters of 70–120 nm. The morphologies of LdANPs/EGNFs@PE substrates were studied by energy-dispersive x-ray spectroscopy (EDX), scanning electron microscopy (SEM), and x-ray fluorescence (XRF). The mechanical properties of the prepared polyethylene plastic were enhanced by reinforcement with glass nanofibers as a roughening agent. The photoluminescent substrates showed markedly improved scratch resistance in comparison to LdANPs-free EGNFs@PE substrate. The obtained luminescence spectra displayed an emission band at 519 nm upon excitation at 365 nm. The results demonstrated that the luminous plastic has improved hydrophobicity and UV shielding upon increasing the LdANPs content.