Evaluation of six k-ε turbulence model predictions of flow in a continuous casting billet-mold water model using laser doppler velocimetry measurements

Quantitative fluid velocity and turbulence measurements, which were obtained using laser Doppler velocimetry (LDV) during the course of the physical modeling of the molten steel flow in a continuous casting billet mold, are presented. The experimental findings are compared to the results obtained by the computational simulation of the flow using the high-Reynolds-number and five low-Reynolds-number k-ε turbulence models. The predictive capabilities of these turbulence models were assessed in comparison to the experimentally measured axial velocity and turbulence kinetic energy values. The measured turbulence velocity fluctuations in various directions strongly suggest the nonisotropic nature of the flow field, which cannot be predicted with the k-ε models. The extent of deviations between the measurements and computations was quantified.     

Cerium oxide/SnO2-based semiconductor gas sensors with improved sensitivity to CO

SnO₂-based semiconductor gas sensors have been successfully fabricated and tested for detecting carbon monoxide and methane. The sensitivity and selectivity of the sensors are tailored by incorporation of different additives such as platinum and cerium oxide. While platinum enhances the sensor response to CH₄, ceria suppresses its sensitivity in favor of carbon monoxide. The effect of operating temperature on the performance of sensors is reported. Addition of 10% cerium oxide in the SnO₂ sample leads to an insignificant response to methane even at an elevated temperature of 450°C, while its response to CO remains intact.     

Probabilistic elderly person’s mood analysis based on its activities of daily living using smart facilities

Pattern Analysis and Applications - The world's population is aging, and eldercare services that use smart facilities such as smart homes are widely common in societies now. With the aid of...     

DABPR: a large-scale internet of things-based data aggregation back pressure routing for disaster management

In this paper, we propose a data aggregation back pressure routing (DABPR) scheme, which aims to simultaneously aggregate overlapping routes for efficient data transmission and prolong the lifetime of the network. The DABPR routing algorithm is structured into five phases in which event data is sent from the event areas to the sink nodes. These include cluster-head selection, maximization of event detection reliability, data aggregation, scheduling, and route selection with multi attributes decision making metrics phases. The scheme performs data aggregation on redundant data at relay nodes in order to decrease both the size and rate of message exchanges to minimize communication overhead and energy consumption. The proposed scheme is assessed in terms of packet delivery, network lifetime, ratio, energy consumption, and throughput, and compared with two other well-known protocols, namely “information-fusion-based role assignment (InFRA)” and “data routing for in-network aggregation (DRINA)”, which intrinsically are cluster and tree-based routing schemes designed to improve data aggregation efficiency by maximizing the overlapping routes. Meticulous analysis of the simulated data showed that DABPR achieved overall superior proficiency and more reliable performance in all the evaluated performance metrics, above the others. The proposed DABPR routing scheme outperformed its counterparts in the average energy consumption metric by 64.78% and 51.41%, packet delivery ratio by 28.76% and 16.89% and network lifetime by 42.72% and 20.76% compared with InFRA and DRINA, respectively.

     

A flow matrix-based heuristic algorithm for cell formation and layout design in cellular manufacturing system

Cellular manufacturing is a successful application of group technology (GT) concepts. The aim of a cellular manufacturing system (CMS) is to identify similar manufacturing processes and features where machines are grouped into machine cells based on their contributions to the production process. In the last three decades of research in cell formation, researchers have mainly used zero-one machine component incidence matrix as the input data for the problem. However, recently efforts have been made to use other data structures, such as interval data and ordinal data (consisting of sequence of processing). Sequence data provide valuable information about the flow patterns of various jobs in a manufacturing system. This study develops a heuristic algorithm based on flow matrix for cell formation and layout design in a simultaneous fashion using sequence data. The numerical results of the algorithm on the available problems in the literature indicate the usefulness of the algorithm regarding to performance indices.     

Barium carbonate nanostructures: Biosynthesis and their biomedical applications

In recent years, nanostructures and nanoarchitectures have attracted much attention in the development of biomedicine and nanomedicine. The plant-mediated synthesis of barium carbonate nanoparticles (BACN) has been performed using barium chloride and aqueous extract of natural sweetener (Stevia). In this study, the biosynthesis of BACN has been selected due to the useful medicinal potentials and suitable obtained biocompatibility of produced nanoparticles as well as its simplicity, lesser production steps, and cost-effectiveness. Barium carbonate nanostructures were characterized by X-ray diffraction, scanning electron microscopy, and transmission electron microscope. The toxicity of BACN on U87 brain cancer cells was evaluated based on MTT assay. According to the results, the prepared nanostructures can be employed for biomedical applications, especially cancer therapy.     

Enabling remote-control for the power sub-stations over LTE-A networks

Telecommunication Systems - In recent years, smart grid (SG) applications have been proven a sophisticated technology of immense aptitude, comfort and efficiency not only for the power generation...     

The effect of keyword repetition in abstract and keyword frequency per journal in predicting citation counts

This paper investigates an association between two new variables and citations in papers. These variables include the abstract ratio (the sum of repetition of keywords in abstract divided by abstract length) and the weight ratio (the frequency of paper’s keyword per journal). The data consist of 5875 papers from 12 journals in education: three journals from each SCImago quartile. The researchers used semi-continuous regression to model the data and measure the impact of the proposed variables on citations. The results revealed that both abstract ratio and weight ratio are statistically significant predictors of citations in scientific articles in education.