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Partial least squares path modelling (PLSPM)
Published 2013“…The use of partial least squares path modelling (PLSPM) has escalated in the areas of marketing, management, information systems, and organizational behaviour. …”
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Examining a Supply-Side Predictive Model in Tourism using Partial Least Squares Path Modeling
Published 2012“…Based on data from 154 countries, partial least squares path modeling (PLSPM) was used to test a predictive model that examines causal relationships among the environment, economy, infrastructure, and tourism demand at the aggregate country level. …”
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NETWORK DEPLOYMENT AND UAV PATH PLANNING IN UAV-ASSISTED IOT NETWORKS: A JOINT OPTIMIZATION MODEL
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Path Independence of Exotic Options and Convergence of Binomial Approximations
Published 2019“…Furthermore, we derive a closed form formula for the coefficient of 1∕ in the expansion of the error of our path-independent pricing when the underlying is approximated by the Cox, Ross, and Rubinstein (CRR) model. …”
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A neural networks algorithm for data path synthesis
Published 2003“…This paper presents a deterministic parallel algorithm to solve the data path allocation problem in high-level synthesis. …”
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Stable path of interacting crack systems and micromechanics of damage
Published 2018“…The incremental entropy, which can be expressed in terms of second order work, is then calculated for various points on the response paths of some typical symmetric crack systems. It is shown that while the symmetric states may be stable, the path which leads to them is unstable and cannot occur in reality. …”
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A method for data path synthesis using neural networks
Published 2017“…The method is based on the modified Hopfield neural network model of computation and the McCulloch-Pitts binary neuron model. …”
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Effect of non-cooperative vehicles on path connectivity in vehicular networks
Published 2018“…However, the existence of an end-to-end multi-hop path across VANET highly depends on the willingness of vehicles to cooperate with one another when it comes to data forwarding. …”
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Priority and Path Diversity Based Video Streaming over Best Effort Networks
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Millimeter Wave Channel Characterization for 5G Cellular Systems in the UAE at the 28 GHz Band
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Formulation of heat conduction and thermal conductivity of metals
Published 2019“…Potential applications of this approach include extension of the present kinetic model incorporating quantum effects to cases in which electron scattering occurs such as in nanowires and hollow nanowires.…”
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On Hughes' Model for Pedestrian Traffic
Published 2020“…Hughes in 2002, a system of a hyperbolic conservation law to describe crowd densities (ρ) and an Eikonal equation to describe the path potential (φ) of this crowd: ρt − div(ρf 2(ρ)∇φ) = 0 ||∇φ|| =1 f(p) Throughout this work, we explain this model in the context of a one dimensional walking facility, like a bridge or a hallway, and a two dimensional one, such as an open room with obstacles and obstructions. …”
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Comprehensive review of gas turbine fault diagnostic strategies
Published 2025“…Additionally, it presents a comprehensive review of proposed diagnostic approaches, including model-based, data-driven, and advanced hybrid techniques developed for each category of faults. …”
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Extending a tourism causality network model
Published 2011“…Specifically, this study considered an a priori validated structural model for relationships among the economy, society, environment and tourism constructs in 162 countries, examining the extent to which the model was invariant across two groups of isolated observations: developed and less-developed countries. …”
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Modeling the effects of capillary pressure with the presence of full tensor permeability and discrete fracture models using the mimetic finite difference method
Published 2021“…In this paper, we focus on modeling and quantifying the oil recovered from NFR’s through the imbibition processes using a novel fully implicit mimetic finite difference (MFD) approach coupled with discrete fracture/discrete matrix (DFDM) technique. …”
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A Hybrid Deep Learning Model Using CNN and K-Mean Clustering for Energy Efficient Modelling in Mobile EdgeIoT
Published 2023“…<p dir="ltr">In mobile edge computing (MEC), it is difficult to recognise an optimum solution that can perform in limited energy by selecting the best communication path and components. This research proposed a hybrid model for energy-efficient cluster formation and a head selection (E-CFSA) algorithm based on convolutional neural networks (CNNs) and a modified k-mean clustering (MKM) method for MEC. …”