Nanoparticle shape and thermal radiation on Marangoni Water, Ethylene Glycol and Engine Oil Based Cu, Al2O3 and SWCNTs

The aim of this paper is to investigate the relationship between particle shape and radiation effects on Marangoni boundary layer flow and heat transfer of water, ethylene glycol and engine oil based Cu, Al2O3 and SWCNTs. There are three types of nanoparticle shapes are considered in this research such as sphere, cylinder and lamina. The governing nonlinear partial differential equations are reduced into a set of nonlinear ordinary differential equations by applying similarity transformation which is solved using shooting technique in conjunction with Newton’s method and Runge Kutta algorithm. Temperature profiles are graphically and tabularly provided for the effects of solid volume fraction parameter, radiation parameter and empirical shape factor. The result shows that solid volume fraction and radiation energy gives a good impact on thermal boundary layer. Sphere nanoparticle shape predicts a better result on heat transfer rather than other nanoparticle shapes

Corrosion assessment on reinforced concrete and its service life prediction

Deterioration of structural concrete may be caused either by chemical or physical effects. Corrosion of embedded steel is a major cause of deterioration of concrete structures at the present time. This lead to structural weakening due to loss of steel cross-section, surface staining, cracking or spalling and delamination of concrete and then gradually reduces the service life of the reinforced concrete structures. The most biggest problem is concerned with the structural integrity and safety of reinforced concrete structures by reducing the load carrying capacity. This project was to assess the degree of corrosion on reinforced concrete structure and estimating the residual service life. It was conducted based on electrochemical methods. These methods include galvanostatic pulse method and linear polarization method. A Non-Destructive Test techniques called GalvaPulse was used in this study. These equipments allow us to determine the degree of corrosion, rate of corrosion and interpret the result in corrosion mapping. From the results, assessment on the validation of corrosion in short and long terms by using predictive models are discussed

Online Model Evaluation in a Large-Scale Computational Advertising Platform

Online media provides opportunities for marketers through which they can deliver effective brand messages to a wide range of audiences. Advertising technology platforms enable advertisers to reach their target audience by delivering ad impressions to online users in real time. In order to identify the best marketing message for a user and to purchase impressions at the right price, we rely heavily on bid prediction and optimization models. Even though the bid prediction models are well studied in the literature, the equally important subject of model evaluation is usually overlooked. Effective and reliable evaluation of an online bidding model is crucial for making faster model improvements as well as for utilizing the marketing budgets more efficiently. In this paper, we present an experimentation framework for bid prediction models where our focus is on the practical aspects of model evaluation. Specifically, we outline the unique challenges we encounter in our platform due to a variety of factors such as heterogeneous goal definitions, varying budget requirements across different campaigns, high seasonality and the auction-based environment for inventory purchasing. Then, we introduce return on investment (ROI) as a unified model performance (i.e., success) metric and explain its merits over more traditional metrics such as click-through rate (CTR) or conversion rate (CVR). Most importantly, we discuss commonly used evaluation and metric summarization approaches in detail and propose a more accurate method for online evaluation of new experimental models against the baseline. Our meta-analysis-based approach addresses various shortcomings of other methods and yields statistically robust conclusions that allow us to conclude experiments more quickly in a reliable manner. We demonstrate the effectiveness of our evaluation strategy on real campaign data through some experiments.Comment: Accepted to ICDM201

Characterisation of materials through x-ray mapping

Scanning electron microscopy (SEM) energy dispersive spectroscopy (EDS, wavelength dispersive spectroscopy (WDS) and the conbination of these techniques through x-ray mapping (XRM) have become excellent tool for characterising the distribution of elements and phases in materials. Quantitative x-ray mapping (QXRM) enables reliable quantitative results that cna be an order of magnitude better than traditional analysis and is also far superior to regions of interest x-ray maps(ROIM) where low levels of an element overlaps are present

X-ray mapping and post processing

Characterisation of materials frequently involves the determination of variation in composition, structure and microstructure, by the use of a variety of imaging and analysis techniques. There is an increasing need to understand materials phenomena and processes and to learn more about exploiting subtle changes in the distribution of elements in materials technology. Scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), wavelength dispersive spectroscopy (WDS) and the combination of these techniques through x-ray mapping (XRM) has become an excellent tool for characterising the distribution of elements and phases in materials. This analytical technique provides a high magnification image related to the distribution and relative abundance of elements within a given specimen and thus makes XRM particularly useful for: • identifying the location of individual elements and • mapping the spatial distribution of specific elements and phases within a sample (material surface). Quantitative x-ray mapping (QXRM) enables reliable quantitative results that can be an order of magnitude better than traditional analysis and is also far superior to regions of interest x-ray maps (ROIM) where low levels of an element or elemental overlaps are present

A new biased model order reduction for higher order interval systems

This paper presents a new biased method for order reduction of linear continuous time interval systems. This method is based on the Stability equation method, Pade approximation and Kharitonov’s theorem. The higher order interval system is represented by four Kharitonov transfer functions using the Kharitonov’s theorem, and then reduced order models are obtained by the general form of the Stability equation method and Pade approximation. The Stability equation method is used to obtain a reduced order denominator polynomial while the Pade approximation is used for reduced order numerator coefficients. This method generates a stable reduced order model if the original higher order interval system is stable. The proposed method is illustrated with the help of typical numerical examples considered from the literature, and these are compared with well-known methods to show the efficacy of the proposed method

Computational structures for robotic computations

The computational problem of inverse kinematics and inverse dynamics of robot manipulators by taking advantage of parallelism and pipelining architectures is discussed. For the computation of inverse kinematic position solution, a maximum pipelined CORDIC architecture has been designed based on a functional decomposition of the closed-form joint equations. For the inverse dynamics computation, an efficient p-fold parallel algorithm to overcome the recurrence problem of the Newton-Euler equations of motion to achieve the time lower bound of O(log sub 2 n) has also been developed

Spartan Daily, January 6, 1960

Volume 47, Issue 58https://scholarworks.sjsu.edu/spartandaily/3974/thumbnail.jp