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Article
Publication date: 13 June 2024

Ryley McConkey, Nikhila Kalia, Eugene Yee and Fue-Sang Lien

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be…

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Abstract

Purpose

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. This paper aims to address this issue by proposing a semi-automated calibration of these coefficients using a new framework (referred to as turbo-RANS) based on Bayesian optimization.

Design/methodology/approach

The authors introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse or dense reference data for the purpose of calibrating RANS turbulence closure model coefficients. Then, the authors describe a Bayesian optimization-based algorithm for conducting the calibration of these model coefficients. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure.

Findings

The authors demonstrate that the performance of the k-ω shear stress transport (SST) and generalized k-ω (GEKO) turbulence models can be efficiently improved via turbo-RANS, for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel.

Originality/value

To the best of the authors’ knowledge, this work is the first to propose and provide an open-source black-box calibration procedure for turbulence model coefficients based on Bayesian optimization. The authors propose a data-flexible objective function for the calibration target. The open-source implementation of the turbo-RANS framework includes OpenFOAM, Ansys Fluent, STAR-CCM+ and solver-agnostic templates for user application.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 34 no. 8
Type: Research Article
ISSN: 0961-5539

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Article
Publication date: 17 July 2019

Ali Ayyed Abdul-Kadhim, Fue-Sang Lien and Eugene Yee

This study aims to modify the standard probabilistic lattice Boltzmann methodology (LBM) cellular automata (CA) algorithm to enable a more realistic and accurate computation of…

171

Abstract

Purpose

This study aims to modify the standard probabilistic lattice Boltzmann methodology (LBM) cellular automata (CA) algorithm to enable a more realistic and accurate computation of the ensemble rather than individual particle trajectories that need to be updated from one time step to the next (allowing, as such, a fraction of the collection of particles in any lattice grid cell to be updated in a time step, rather than the entire collection of particles as in the standard LBM-CA algorithm leading to a better representation of the dynamic interaction between the particles and the background flow). Exploitation of the inherent parallelism of the modified LBM-CA algorithm to provide a computationally efficient scheme for computation of particle-laden flows on readily available commodity general-purpose graphics processing units (GPGPUs).

Design/methodology/approach

This paper presents a framework for the implementation of a LBM for the simulation of particle transport and deposition in complex flows on a GPGPU. Towards this objective, the authors have shown how to map the data structure of the LBM with a multiple-relaxation-time (MRT) collision operator and the Smagorinsky subgrid-scale turbulence model (for turbulent fluid flow simulations) coupled with a CA probabilistic method (for particle transport and deposition simulations) to a GPGPU to give a high-performance computing tool for the calculation of particle-laden flows.

Findings

A fluid-particle simulation using our LBM-MRT-CA algorithm run on a single GPGPU was 160 times as computationally efficient as the same algorithm run on a single CPU.

Research limitations/implications

The method is limited by the available computational resources (e.g. GPU memory size).

Originality/value

A new 3D LBM-MRT-CA model was developed to simulate the particle transport and deposition in complex laminar and turbulent flows with different hydrodynamic characteristics (e.g. vortex shedding, impingement, free shear layer, turbulent boundary layer). The solid particle information is encapsulated locally at the lattice grid nodes, allowing for straightforward mapping of the datastructure onto a GPGPU enabling a massive parallel execution of the LBM-MRT-CA algorithm. The new particle transport algorithm was based on the local (bulk) particle density and velocity and provides more realistic results for the particle transport and deposition than the standard LBM-CA algorithm.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 29 no. 7
Type: Research Article
ISSN: 0961-5539

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Article
Publication date: 5 September 2018

Mengdi Li, Eugene Ch’ng, Alain Yee Loong Chong and Simon See

Recently, various Twitter Sentiment Analysis (TSA) techniques have been developed, but little has paid attention to the microblogging feature – emojis, and few works have been…

1502

Abstract

Purpose

Recently, various Twitter Sentiment Analysis (TSA) techniques have been developed, but little has paid attention to the microblogging feature – emojis, and few works have been conducted on the multi-class sentiment analysis of tweets. The purpose of this paper is to consider the popularity of emojis on Twitter and investigate the feasibility of an emoji training heuristic for multi-class sentiment classification of tweets. Tweets from the “2016 Orlando nightclub shooting” were used as a source of study. Besides, this study also aims to demonstrate how mapping can contribute to interpreting sentiments.

Design/methodology/approach

The authors presented a methodological framework to collect, pre-process, analyse and map public Twitter postings related to the shooting. The authors designed and implemented an emoji training heuristic, which automatically prepares the training data set, a feature needed in Big Data research. The authors improved upon the previous framework by advancing the pre-processing techniques, enhancing feature engineering and optimising the classification models. The authors constructed the sentiment model with a logistic regression classifier and selected features. Finally, the authors presented how to visualise citizen sentiments on maps dynamically using Mapbox.

Findings

The sentiment model constructed with the automatically annotated training sets using an emoji approach and selected features performs well in classifying tweets into five different sentiment classes, with a macro-averaged F-measure of 0.635, a macro-averaged accuracy of 0.689 and the MAEM of 0.530. Compared to those experimental results in related works, the results are satisfactory, indicating the model is effective and the proposed emoji training heuristic is useful and feasible in multi-class TSA. The maps authors created, provide a much easier-to-understand visual representation of the data, and make it more efficient to monitor citizen sentiments and distributions.

Originality/value

This work appears to be the first to conduct multi-class sentiment classification on Twitter with automatic annotation of training sets using emojis. Little attention has been paid to applying TSA to monitor the public’s attitudes towards terror attacks and country’s gun policies, the authors consider this work to be a pioneering work. Besides, the authors have introduced a new data set of 2016 Orlando Shooting tweets, which will be made available for other researchers to mine the public’s political opinions about gun policies.

Details

Industrial Management & Data Systems, vol. 118 no. 9
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 4 September 2019

Mengdi Li, Eugene Chng, Alain Yee Loong Chong and Simon See

Emoji has become an essential component of any digital communication and its importance can be attested to by its sustained popularity and widespread use. However, research in…

2194

Abstract

Purpose

Emoji has become an essential component of any digital communication and its importance can be attested to by its sustained popularity and widespread use. However, research in Emojis is rarely to be seen due to the lack of data at a greater scale. The purpose of this paper is to systematically analyse and compare the usage of Emojis in a cross-cultural manner.

Design/methodology/approach

This research conducted an empirical analysis using a large-scale, cross-regional emoji usage data set from Twitter, a platform where the limited 140 characters allowance has made it essential for the inclusion of emojis within tweets. The extremely large textual data set covers a period of only two months, but the 673m tweets authored by more than 2,081,542 unique users is a sufficiently large sample for the authors to yield significant results.

Findings

This research discovered that the categories and frequencies of Emojis communicated by users can provide a rich source of data to understand cultural differences between Twitter users from a large range of demographics. This research subsequently demonstrated the preferential use of Emojis complies with Hofstede’s Cultural Dimensions Model, in which different representations of demographics and culture within countries present significantly different use of Emojis to communicate emotions.

Originality/value

This study provides a robust example of how to strategically conduct research using large-scale emoji data to pursue research questions previously difficult. To the best of authors’ knowledge, the present study pioneers the first systematic analysis and comparison of the usage of emojis on Twitter across different cultures; it is the largest, in terms of the scale study of emoji usage to-date.

Details

Industrial Management & Data Systems, vol. 119 no. 8
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 10 January 2025

Omkar Dastane, Mun-Yee Ooi, Eugene Cheng-Xi Aw, Wen-Hwa Shyu and Garry Wei-Han Tan

This study integrates the theories of perceived value and human-robot interaction to develop a framework for examining the influence of artificial intelligence-based service…

46

Abstract

Purpose

This study integrates the theories of perceived value and human-robot interaction to develop a framework for examining the influence of artificial intelligence-based service chatbot’s (AI-BOT) attributes on user stickiness (US) in the context of human-centric services. The study specifically examines the mediating role of perceived competence (PC) in the association between AI-BOT attributes and the US. It also examines how perceived empathy (PE) moderates the correlation between PC and US.

Design/methodology/approach

The empirical data was collected through a self-administered questionnaire from 470 respondents with prior experience of interacting with AI-BOTs. The data was analysed using SMART-PLS by performing structural equation modelling.

Findings

The study found a positive and significant impact of selected AI-BOT attributes on US. Among all selected attributes, personalization had the strongest impact on PC and recommendations had the strongest impact on US. Responsiveness did not emerge as a contributory factor for generating PC. This paper confirmed the mediating role of PC among relationships between selected attributes and US but such mediation was minor. PE moderated the relationship between PC and US negatively.

Originality/value

This study provides novel insights by identifying that PE dampens the relationship between PC and US. Additionally, it provides a framework to stimulate the US for AI-BOTs by combining technical aspects (human-computer interaction theory) with value aspects (theory of perceived value) and by positioning constructs specific to human-centric services. All in all, the study offers a dual-layered perspective regarding value-in-use resulting in a comprehensive understanding of human-technology interactions during human-centric service encounters.

Details

Journal of Consumer Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0736-3761

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Article
Publication date: 4 April 2016

Alain Yee Loong Chong, Boying Li, Eric W.T. Ngai, Eugene Ch'ng and Filbert Lee

The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user…

10288

Abstract

Purpose

The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales.

Design/methodology/approach

The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales.

Findings

This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume.

Originality/value

This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies.

Details

International Journal of Operations & Production Management, vol. 36 no. 4
Type: Research Article
ISSN: 0144-3577

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Article
Publication date: 5 June 2017

Eric R. Kushins, Henry Heard and J. Michael Weber

This article proposes a new disruptive innovation in healthcare through the development of a physician assistant business model, which can be most readily applied in vulnerable…

849

Abstract

Purpose

This article proposes a new disruptive innovation in healthcare through the development of a physician assistant business model, which can be most readily applied in vulnerable rural health care settings.

Design/methodology/approach

This study reviews the current state of the health care system in terms of physician assistant utilization and primary care shortages in rural communities. The study proposes that the physician assistant-owned and -operated primary care business represents a disruptive innovation, via the application of the five principles of Clayton Christensen’s (1997) thesis on disruptive innovation.

Findings

Considering the current state of the health care industry, the study logically defends the proposed model as a disruptive innovation in that it: focuses on an underserved market, has lower costs, has few competitors, offers high quality and provides a sustainable competitive advantage.

Practical implications

The physician assistant business model is a viable solution for providing primary care for rural communities with educational, financial, transportation and other resource limitations.

Originality/value

This is a unique application of the theory of disruptive innovation, which illustrates how a new business model can solve a chronic shortage in primary care, especially in underserved populations.

Details

International Journal of Pharmaceutical and Healthcare Marketing, vol. 11 no. 2
Type: Research Article
ISSN: 1750-6123

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Article
Publication date: 1 February 1993

Thomas A. Peters

The purpose of this article is to present an overview of the history and development of transaction log analysis (TLA) in library and information science research. Organizing a…

579

Abstract

The purpose of this article is to present an overview of the history and development of transaction log analysis (TLA) in library and information science research. Organizing a literature review of the first twenty‐five years of TLA poses some challenges and requires some decisions. The primary organizing principle could be a strict chronology of the published research, the research questions addressed, the automated information retrieval (IR) systems that generated the data, the results gained, or even the researchers themselves. The group of active transaction log analyzers remains fairly small in number, and researchers who use transaction logs tend to use this method more than once, so tracing the development and refinement of individuals' uses of the methodology could provide insight into the progress of the method as a whole. For example, if we examine how researchers like W. David Penniman, John Tolle, Christine Borgman, Ray Larson, and Micheline Hancock‐Beaulieu have modified their own understandings and applications of the method over time, we may get an accurate sense of the development of all applications.

Details

Library Hi Tech, vol. 11 no. 2
Type: Research Article
ISSN: 0737-8831

Available. Content available
Book part
Publication date: 25 November 2019

Nathan Hulsey

Free Access. Free Access

Abstract

Details

Games in Everyday Life: For Play
Type: Book
ISBN: 978-1-83867-937-8

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Book part
Publication date: 16 September 2013

Kathleen Spring and Brenda DeVore Marshall

This chapter discusses Launching through the Surf: The Dory Fleet of Pacific City, a project which documents the historical and contemporary role of dory fishers in the life of…

Abstract

This chapter discusses Launching through the Surf: The Dory Fleet of Pacific City, a project which documents the historical and contemporary role of dory fishers in the life of the coastal village of Pacific City, Oregon, U.S. Linfield College’s Department of Theatre and Communication Arts, its Jereld R. Nicholson Library, the Pacific City Arts Association, the Pacific City Dorymen’s Association, and the Linfield Center for the Northwest joined forces to engage in a collaborative college and community venture to preserve this important facet of Oregon’s history. Using ethnography as a theoretical grounding and oral history as a method, the project utilized artifacts from the dory fleet to augment interview data, and faculty/student teams created a searchable digital archive available via open access. The chapter draws on the authors’ experiences to identify a philosophy of strategic collaboration. Topics include project development and management, assessment, and the role of serendipity. In an era of value-added services where libraries need to continue to prove their worth, partnering with internal and external entities to create content is one way for academic libraries to remain relevant to agencies that do not have direct connections to higher education. This project not only developed a positive “town and gown” relationship with a regional community, it also benefited partner organizations as they sought to fulfill their missions. The project also serves as a potential model for intra- and inter-agency collaboration for all types of libraries.

Details

Mergers and Alliances: The Operational View and Cases
Type: Book
ISBN: 978-1-78350-054-3

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