Shear wave propagation in vertically heterogeneous viscoelastic layer over a micropolar elastic half-space

This article deals with the propagation of SH-wave in a vertically heterogeneous viscoelastic layer lying over a micropolar elastic half-space. Dispersion and damping equations are obtained analytically in closed form. Phase and damped velocities are computed numerically and depicted by means of a graph to exhibit the substantial effect of heterogeneity, viscoelasticity (internal friction), and micropolar parameter. As a special case of the problem, it is found that deduced dispersion relation is well in agreement to the classical-Love wave equation and damping equation vanishes identically for the isotropic case. Influence of micropolarity present in the medium of half-space is highlighted through comparative study.     

Effect of undulation on SH-wave propagation in corrugated magneto-elastic transversely isotropic layer

This article delves to study the effect of corrugated boundary surfaces on the propagation of horizontally-polarized shear waves (SH-waves) in a magnetoelastic transversely isotropic layer under a hydrostatic state of stress lying over an elastic half-space under gravity. A dispersion equation has been derived in closed-form and is found to be in good agreement to the classical Love-wave equation. The effect of magnetoelasticity, hydrostatic state of stress, gravity, corrugation, position parameter, and undulation on the phase velocity of the SH-wave has been identified. Numerical computation along with graphical demonstration has been carried out for cadmium, magnesium, and zinc materials of hexagonal symmetry to highlight some significant facts.     

Managing the degree of exfoliation and number of graphene layers using probe sonication approach

We have demonstrated a fast, versatile, and scalable approach to synthesize high-quality few layer graphene sheets with low defect ratio and high crystallinity produced from exfoliation of graphite flakes in DMF by using probe sonication. The effect of sonication time on degree of exfoliation and number of graphene layers has been fully investigated. The degree of exfoliation of graphene sheets as a function of sonication time has been successfully analyzed by XRD, UV-Vis spectroscopy, TEM, and BET studies. The morphological changes at different sonication times have also been observed by SEM. A structural and defect characterization of graphene sheets has been discussed in detail by Raman spectroscopic technique. The shift in position of 2D Raman band and its de-convolution provided information about formation of multi to few layer graphene sheets with sonication. Moreover, Raman results are highly consistent with TEM studies as per number of graphene layers is concerned.     

Empirical validation of object-oriented metrics for predicting fault proneness models

Empirical validation of software metrics used to predict software quality attributes is important to ensure their practical relevance in software organizations. The aim of this work is to find the relation of object-oriented (OO) metrics with fault proneness at different severity levels of faults. For this purpose, different prediction models have been developed using regression and machine learning methods. We evaluate and compare the performance of these methods to find which method performs better at different severity levels of faults and empirically validate OO metrics given by Chidamber and Kemerer. The results of the empirical study are based on public domain NASA data set. The performance of the predicted models was evaluated using Receiver Operating Characteristic (ROC) analysis. The results show that the area under the curve (measured from the ROC analysis) of models predicted using high severity faults is low as compared with the area under the curve of the model predicted with respect to medium and low severity faults. However, the number of faults in the classes correctly classified by predicted models with respect to high severity faults is not low. This study also shows that the performance of machine learning methods is better than logistic regression method with respect to all the severities of faults. Based on the results, it is reasonable to claim that models targeted at different severity levels of faults could help for planning and executing testing by focusing resources on fault-prone parts of the design and code that are likely to cause serious failures.     

Layered axial force coupled membrane based metal contact single-pole quad-throw RF MEMS switch: design,RF performance and mechanical modeling

Microsystem Technologies - RF MEMS is a perfect candidate to replace the conventional switches used for microwave frequency. Most of the RF MEMS switches developed generally concentrate on...     

Comparing and evaluating the effectiveness of mobile Web adequacy evaluation tools

Universal Access in the Information Society - The number of Web sites is growing exponentially and so are the people who are accessing them on mobile devices including people with special...     

Analysis of the Photonic Crystal Power Splitter Based on the Junction Defect Radius for Optimum Resonance

Nano-photonics is an emerging area of optical materials, which would take the optointegrated circuits towards progress. Photonic crystal (PC) based power splitters are useful constituents for the design of photonic integrated circuits (PICs). They are very important devices for connecting different building blocks on an integrated optical chip. In this paper, a two-dimensional PC Y-junction power splitter (21×15 μm) based on the resonance effect with circular air holes etched on a hexagonal lattice with a period a is proposed. The plane wave expansion (PWE) and finite difference time domain (FDTD) techniques are used for analyzing the structure. The simulation results show that the optimum resonance occurs when the radius of the defect hole is 0.3a, leading to the maximum and equal power distribution.     

Learning multiple experiences useful visual features for active maps localization in crowded environments

Crowded urban environments are composed of different types of dynamic and static elements. Learning and classification of features is a major task in solving the localization problem in such environments. This work presents a gradual learning methodology to learn the useful features using multiple experiences. The usefulness of an observed element is evaluated by a scoring mechanism which uses two scores – reliability and distinctiveness. The visual features thus learned are used to partition the visual map into smaller regions. The robot is efficiently localized in such a partitioned environment using two-level localization. The concept of active map (AM) is proposed here, which is a map that represents one partition of the environment in which there is a high probability of the robot existing. High-level localization is used to track the mode of the AMs using discrete Bayes filter. Low-level localization uses a bag-of-words model to retrieve images and accurately localize the robot. The pose of the robot is the one retrieved from the AM that has maximum a posteriori. Experiments have been conducted on a unique highly crowded data-set collected from Indian roads. The results support the proposed method due to speed and localization accuracy.     

Can cities become self-reliant in food?

Modern cities almost exclusively rely on the import of resources to meet their daily basic needs. Food and other essential materials and goods are transported from long-distances, often across continents, which results in the emission of harmful greenhouse gasses. As more people now live in cities than rural areas and all future population growth is expected to occur in cities, the potential for local self-reliance in food for a typical post-industrial North American city was determined. Given current policies and bylaws and available area, crop yields, and human intake, three distinct scenarios were developed to determine the potential level of food self-reliance for the City of Cleveland, which has been plagued with home foreclosures and resulting vacant land, lack of access to healthy food, hunger, and obesity particularly in disadvantaged neighborhoods. Scenario I, which utilizes 80% of every vacant lot, can generate between 22% and 48% of Cleveland’s demand for fresh produce (vegetables and fruits) depending on the vegetable production practice used (conventional gardening, intensive gardening, or hydroponics), 25% of both poultry and shell eggs, and 100% of honey. Scenario II, which uses 80% of every vacant lot and 9% of every occupied residential lot, can generate between 31% and 68% of the needed fresh produce, 94% of both poultry and shell eggs, and 100% of honey. Finally, scenario III, which adds 62% of every industrial and commercial rooftop in addition to the land area used in scenario II, can meet between 46% and 100% of Cleveland’s fresh produce need, and 94% of poultry and shell eggs and 100% of honey. The three scenarios can attain overall levels of self-reliance between 4.2% and 17.7% by weight and 1.8% and 7.3% by expenditure in total food and beverage consumption, compared to the current level of 0.1% self-reliance in total food and beverage by expenditure. The analysis also reveals that the enhanced food self-reliance would result in $29 M to $115 M being retained in Cleveland annually depending upon the scenario employed. This study provides support to the hypothesis that significant levels of local self-reliance in food, the most basic need, is possible in post-industrial North American cities. It is concluded that while high levels of local self-reliance would require an active role of city governments and planners, public commitment, financial investment, and labor, the benefits to human health, the local and global environment, and the local economy and community may outweigh the cost.