Correction to: Double Vision Model Using Space-Time Function Control within Silicon Microring System

Silicon - The original version of this article unfortunately contained a mistake in the “Acknowledgments” section.     

Melting heat transfer analysis of electrically conducting nanofluid flow over an exponentially shrinking/stretching porous sheet with radiative heat flux under a magnetic field

Modern magnetic nanomaterial processing operations are progressing rapidly and require increasingly sophisticated mathematical models for their optimization. Stimulated by such developments, in this paper, a theoretical and computational study of a steady magnetohydrodynamic nanofluid over an exponentially stretching/shrinking permeable sheet with melting (phase change) and radiative heat transfer is presented. Besides, wall transpiration, that is, suction and blowing (injection), is included. This study deploys Buongiorno's nanofluid model, which simulates the effects of the Brownian motion and thermophoresis. The transport equations and boundary conditions are normalized via similarity transformations and appropriate variables, and the similarity solutions are shown to depend on the transpiration parameter. The emerging dimensionless nonlinear coupled ordinary differential boundary value problem is solved numerically with the Newton-Fehlberg iteration technique. Validation with special cases from the literature is included. The increase in the magnetic field, that is, the Hartmann number, is observed to elevate nanoparticle concentration and temperature, whereas it dampens the velocity. Higher values of the melting parameter consistently decelerate the boundary layer flow and suppress temperature and nanoparticle concentration. A higher radiative parameter strongly increases temperature (and thermal boundary layer thickness) and weakly accelerates the flow. The increase in the Brownian motion reduces nanoparticle concentrations, whereas a greater thermophoretic body force strongly enhances them. The Nusselt number and Sherwood number are observed to be decreased with an increasing Hartmann number, whereas they are elevated with a stronger wall suction and melting parameter.     

Comparison of point cloud data and 3D CAD data for on-site dimensional inspection of industrial plant piping systems

Inspection is vital in industrial plant construction and management. However, traditional inspection methods that rely on human involvement and paper documentation are becoming untenable as modern industrial plants are becoming larger and more complex than legacy facilities. Hence, an efficient and robust method is required to support the inspection of modern industrial plants. In this paper, an improved technique relying on terrestrial laser scanning (TLS) for data acquisition and normal-based region growing and efficient random sample consensus (RANSAC) for point cloud data processing is proposed for the on-site dimensional inspection of the piping systems of an industrial plant. Consequently, the as-built condition of the plant is assessed via a distance-based deviation analysis and a comparison of geometric parameters between the as-designed and as-built models. The method is validated using a dataset acquired from a compartment of a ship has verified the robustness and reliability of the proposed approach.     

Design of a Secure Encryption Model (SEM) for Cloud Data Storage Using Hadamard Transforms

Wireless Personal Communications - Data storage has been one of the most prevalent services that has been offered by cloud computing. One of the challenges associated with data storage has been the...     

Convolutional neural network with adaptive inferential framework for skeleton-based action recognition

In the task of skeleton-based action recognition, CNN-based methods represent the skeleton data as a pseudo image for processing. However, it still remains as a critical issue of how to construct the pseudo image to model the spatial dependencies of the skeletal data. To address this issue, we propose a novel convolutional neural network with adaptive inferential framework (AIF-CNN) to exploit the dependencies among the skeleton joints. We particularly investigate several initialization strategies to make the AIF effective with each strategy introducing the different prior knowledge. Extensive experiments on the dataset of NTU RGB+D and Kinetics-Skeleton demonstrate that the performance is improved significantly by integrating the different prior information. The source code is available at: https://github.com/hhe-distance/AIF-CNN.     

Novel Fault Management Framework Using Markov Chain in Wireless Sensor Networks: FMMC

Wireless Personal Communications - Due to using wireless sensor nodes (WSNs) in inaccessible areas and applying limitations in making nodes to reduce costs, these networks are prone to faults. The...     

Why do people purchase virtual goods? A uses and gratification (U&G) theory perspective

Few literature studies have investigated the relationships between different uses and gratifications (U&Gs) of mobile instant messaging (MIM) apps, continuation, and purchase intentions. To address this gap, the researchers aimed to examine the influence of the content, social, process, and technology U&Gs of MIM on continuation intentions toward MIMs, and purchase intentions toward virtual goods available on MIMs. A comprehensive research model was developed based on the U&G theory, which was tested using cross-sectional data from 309 Japanese MIM users. The study considered six different U&Gs of MIM as independent variables and purchase intentions towards stickers and continuation intentions towards MIM as dependent variables. The study results suggest that exposure U&G has a significant positive association with MIM sticker purchase intentions. The entertainment and affection U&G are positively associated with continuation intentions towards MIM use. The study contributes to the literature by investigating U&Gs that motivate MIM users to have both positive purchase intentions toward virtual goods, such as stickers, and continuation intentions toward MIMs. The study has significant theoretical and practical implications for both researchers and practitioners who are interested in virtual goods, the virtual economy, MIM apps, social media, new media, and the service economy.     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

Time Domain Nuclear Magnetic Resonance (TD-NMR) to evaluate the effect of potato cell membrane electroporation

Time Domain-Nuclear Magnetic Resonance (TD-NMR) was used here for the first time to analyze potato specimens cut from tubers before and after electroporation. The proton transverse relaxation time, T₂, was used to identify the modifications that occur at the cell level involving water molecules mobility in potato tubers after the electroporation treatment. Since electroporation modifies the tissue conductivity, the samples were also analyzed in terms of conductivity, water content, and microscopic morphology of the tissue. Comparison analyses were performed on dried potato (zero water content) and water-potato starch mixtures with different water contents. All the data confirm that the effect of the electroporation process can be identified with a variation of the position of the peaks in T₂ distribution, associated to sub-cellular modifications.