Search Results - "Samaranayake, V.A."

Classifying critical factors that influence community acceptance of mining projects for discrete choice experiments in the United States by Que, Sisi, Awuah-Offei, Kwame, Samaranayake, V.A.

“…Local community acceptance is a key indicator of the socio-political risk associated with a mining project. Discrete choice modeling could enhance stakeholder…”
Get full text

Individual preferences for mineral resource development: Perspectives from an urban population in the United States by Que, Sisi, Awuah-Offei, Kwame, Wang, Liang, Samaranayake, V.A., Weidner, Nathan, Yuan, Shaochun

“…The literature on mining community preferences for mineral development, which is the basis for engaging local communities, mainly focuses on rural communities,…”
Get full text

A Hierarchical Dimension Reduction Approach for Big Data with Application to Fault Diagnostics by Krishnan, R., Samaranayake, V.A., Jagannathan, S.

“…About four zetta bytes of data, which falls into the category of big data, is generated by complex manufacturing systems annually. Big data can be utilized to…”
Get full text

Iron phosphate glass waste forms for vitrifying Hanford AZ102 low activity waste (LAW), part I: Glass formation model by Ray, Chandra S., Samaranayake, V.A., Mohammadkhah, Ali, Day, Thomas E., Day, Delbert E.

“…A methodology for determining glass formation in a 5-component iron phosphate base glass system that contained P2O5, Fe2O3, Al2O3, Na2O and SO3 has been…”
Get full text

Iron phosphate glass waste forms for vitrifying Hanford AZ102 Low Activity Waste (LAW), part II: Property-composition model by Ray, Chandra S., Samaranayake, V.A., Mohammadkhah, Ali, Day, Thomas E., Day, Delbert E.

“…Mathematical models for the chemical durability–composition relation for 5-component iron phosphate glasses, containing a nuclear waste similar to that of the…”
Get full text

Bootstrap-based unit root tests for higher order autoregressive models with GARCH(1, 1) errors by Zhong, Xiao, Samaranayake, V.A.

“…Bootstrap-based unit root tests are a viable alternative to asymptotic distribution-based procedures and, in some cases, are preferable because of the serious…”
Get full text

Mass deployment of sustainable transportation: evaluation of factors that influence electric vehicle adoption by Egbue, Ona, Long, Suzanna, Samaranayake, V. A.

“…Mass penetration of electric vehicles into the market will have a number of impacts and benefits, including the ability to substantially reduce greenhouse gas…”
Get full text

Asymptotic properties of sieve bootstrap prediction intervals for processes by Rupasinghe, Maduka, Samaranayake, V.A.

Get full text

Tree Sampling as a Method to Assess Vapor Intrusion Potential at a Site Characterized by VOC-Contaminated Groundwater and Soil by Wilson, Jordan L, Limmer, Matthew A, Samaranayake, V. A, Schumacher, John G, Burken, Joel G

“…Vapor intrusion (VI) by volatile organic compounds (VOCs) in the built environment presents a threat to human health. Traditional VI assessments are often…”
Get full text

Predicting lifespan of Drosophila melanogaster: A novel application of convolutional neural networks and zero‐inflated autoregressive conditional Poisson model by Zhang, Yi, Samaranayake, V.A., Olbricht, Gayla R., Thimgan, Matthew

“…A model to classify the lifespan of Drosophila, the fruit fly, into short‐ and long‐lived categories based on a sleep characteristic, extracted from activity…”
Get full text

Crash frequency modeling using negative binomial models: An application of generalized estimating equation to longitudinal data by Mohammadi, Mojtaba A., Samaranayake, V.A., Bham, Ghulam H.

“…The prediction of crash frequency models can be improved when several years of crash data are utilized, instead of three to five years of data most commonly…”
Get full text

Asymptotic properties of sieve bootstrap prediction intervals for FARIMA processes by Rupasinghe, Maduka, Samaranayake, V.A.

“…The sieve bootstrap is a resampling technique that uses autoregressive approximations of order p to model invertible linear time series, where p is allowed to…”
Get full text

Iron phosphate glass waste forms for vitrifying Hanford AZ102 low activity waste (LAW), part I: Glass formation model by Ray, Chandra S., Samaranayake, V. A., Mohammadkhah, Ali, Day, Thomas E., Day, Delbert E.

“…Not provided…”
Get full text

Enhancing engineering education using project-based learning for Lean and Six Sigma by Kanigolla, Dinesh, A. Cudney, Elizabeth, M. Corns, Steven, Samaranayake, V.A.

“…Purpose – The aim of this research is to determine the importance and impact of project-based learning (PBL) on students' knowledge in Lean and Six Sigma…”
Get full text

Tensile characterization of glass FRP bars by Kocaoz, S., Samaranayake, V.A., Nanni, A.

“…The characterization of fiber reinforced polymer (FRP) bars for concrete reinforcement is necessary for design purposes as required by structural engineers,…”
Get full text

Obtaining prediction intervals for FARIMA processes using the sieve bootstrap by Rupasinghe, Maduka, Mukhopadhyay, Purna, Samaranayake, V.A.

“…The sieve bootstrap (SB) prediction intervals for invertible autoregressive moving average (ARMA) processes are constructed using resamples of residuals…”
Get full text

A Minimax Approach for Classification with Big-data by Krishnan, R., Jagannathan, S., Samaranayake, V.A.

“…In this paper, a novel methodology to reduce the generalization errors occurring due to domain shift in big data classification is presented. This reduction is…”
Get full text

Distributed Learning of Deep Sparse Neural Networks for High-dimensional Classification by Garg, Shweta, Krishnan, R., Jagannathan, S., Samaranayake, V.A.

“…While analyzing high dimensional data-sets using deep neural network (NN), increased sparsity is desirable but requires careful selection of "sparsity…”
Get full text

A statistical solution to a text decoding challenge problem by Xindi Cai, Rui Xu, Samaranayake, V.A., Wunsch, D.C.

“…Given an encoded unknown text message in the form of a three dimensional spatial series generated by the use of four smooth nonlinear functions, we use a…”
Get full text