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Article
Publication date: 18 August 2022

Hany Osman and Soumaya Yacout

In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical…

Abstract

Purpose

In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.

Design/methodology/approach

Application of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.

Findings

Results achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.

Originality/value

The methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.

Details

Journal of Quality in Maintenance Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 15 July 2022

Radwa Tawfik, Sahar Attia, Ingy Mohamed Elbarmelgy and Tamer Mohamed Abdelaziz

Women's travel pattern is different from those of men. Women who have both paid employment and unpaid care work have more complex travel patterns. However, land-use…

Abstract

Purpose

Women's travel pattern is different from those of men. Women who have both paid employment and unpaid care work have more complex travel patterns. However, land-use policies and urban mobility strategies in the Egyptian context do not consider these differences. This paper analyzes and discusses the travel patterns of the Egyptian working women with children. It examines the difference between men's and women's travel behavior in different income levels. The paper aims at determining the main factors that affect working women's travel patterns within the care economy framework and suggesting recommendations for enhancing women's travel patterns in Greater Cairo Region (GCR).

Design/methodology/approach

The methodology relies on conducting a quantitative and qualitative analysis using questionnaires and interviews with working women and men from different social/economic levels in two different workplaces in GCR.

Findings

The results demonstrate that income level, workplace locations, schools locations, and schools typologies greatly affect working women's travel patterns in GCR.

Originality/value

The study findings will help urban planners and decision-makers to improve working women's mobility to make their daily trips shorter and more accessible to achieve equitable cities through understanding the conducted affecting factors and considering the suggested recommendations.

Details

Archnet-IJAR: International Journal of Architectural Research, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2631-6862

Keywords

Article
Publication date: 1 August 2022

Matthew J.W. McLarnon, Ian R. Gellatly, David A. Richards and Ofer Arazy

Past research on the motivational processes underpinning knowledge sharing has assumed that the sharing processes are similar for all individuals. Yet, sharing is a…

Abstract

Purpose

Past research on the motivational processes underpinning knowledge sharing has assumed that the sharing processes are similar for all individuals. Yet, sharing is a fundamental affiliative behavior, and the sharing processes can differ between people. This study aims to propose and test a model of the moderating influence that employee attachment patterns have on the theory of reasoned action (TRA)-defined knowledge sharing processes.

Design/methodology/approach

The authors administered a questionnaire to 1,103 employees from a range of industries who participated in an online Qualtrics survey. Advanced forms for structural equation modeling and latent profile analysis were used to assess the proposed model.

Findings

The results revealed that participants in the study exhibited the latent profiles corresponding to secure, dismissive, preoccupied and fearful patterns. The preoccupied cohort had the lowest knowledge sharing behavior, yet the strongest links within the sharing process. Secure, dismissive and fearful had similar sharing levels, but the strength of the TRA-defined processes differed. These findings underscore equifinality: although sharing may be approximately equal across different attachment patterns, the fundamental processes underpinning sharing differ.

Research limitations/implications

The authors used self-report data, given that sharing attitudes, norms and intentions may not be overly amenable to ratings even from well-acquainted others. Further, the use of advanced analytical methods helps to minimize common method concerns. Additionally, causal mechanisms underscoring the TRA have been demonstrated (Ajzen and Fishbein, 2005), allowing us to explore the moderating role of attachment patterns.

Practical implications

This study speaks to the importance of considering employees’ attachment patterns, and developing comprehensive intra-organizational norms, policies and systems that support and encourage knowledge sharing from employees with a variety of attachment patterns.

Originality/value

This study uniquely contributes to knowledge sharing literatures by incorporating attachment patterns as moderators within the TRA-defined sharing processes. The authors provide important insights on the role of individuals’ attachment patterns have for knowledge sharing behaviors, but also highlight how structure of knowledge sharing differed across subgroups of employees, determined based on their dispositional attachment pattern.

Details

Journal of Knowledge Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1367-3270

Keywords

Article
Publication date: 15 July 2022

Hashem Aghazadeh and Farzad Zandi

The purpose of the paper is twofold. Firstly, it provides an inclusive categorical framework of internationalisation patterns for small- and medium-sized enterprises, and…

Abstract

Purpose

The purpose of the paper is twofold. Firstly, it provides an inclusive categorical framework of internationalisation patterns for small- and medium-sized enterprises, and secondly, it presents unique evidence from a developing country and corroborates the proposed framework.

Design/methodology/approach

A cross-sectional convergent mixed-method design was applied to this research. A phenomenological design was used to develop the typology framework, and subsequently, practical evidence was collected through a cross-sectional survey using a self-reporting questionnaire. A two-step hierarchical clustering analysis was performed to test the framework’s robustness, and a set of characteristics was compared between the patterns via a one-way analysis of variance, F-test.

Findings

The robustness of the constructed categorical framework of 32 internationalisation patterns is substantiated, suggesting that this framework produces veracious discrimination between all patterns of internationalisation. Evidence revealed that the majority of firms showed regional and incremental internationalising behaviour. No true born globals were observed; however, several other early or fast patterns such as “born-international”, “global new venture” and “born-again regional” emerged.

Originality/value

This paper presents a categorical framework and provides evidence of the behaviour of internationalising SMEs from a Middle Eastern developing economy. The categorical framework constructed in this paper uses predefined thresholds, and it is of value because it is inclusive, rigid and incisive. This paper also provides essential insights into the sub-patterns of internationalisation, specifically the born-again and regional phenomenon of internationalisation.

Details

Journal of Research in Marketing and Entrepreneurship, vol. 24 no. 2
Type: Research Article
ISSN: 1471-5201

Keywords

Article
Publication date: 5 February 2021

Yeonghoon Kang, Jihyun Oh and Sungmin Kim

The development of a parametric garment pattern design system that utilizes anthropometric data for consumer-oriented garment pattern design.

Abstract

Purpose

The development of a parametric garment pattern design system that utilizes anthropometric data for consumer-oriented garment pattern design.

Design/methodology/approach

Action list and interactive user interface were developed to design flat garment patterns. Three-dimensional drape simulation was also implemented to verify the fit of patterns.

Findings

Patterns generated by the parametric design system developed in this study could be modified easily by providing appropriate anthropometric data regardless of their complexities.

Practical implications

Parametric pattern design system can reduce considerable amount of time and cost by replacing the trial-and-error based grading processes.

Social implications

Parametric pattern design system can generate customized garment patterns quickly and easily. Therefore, it is expected to contribute to the production of sustainable fashion and textile by reducing the loss of time and resource.

Originality/value

A versatile and comprehensive action list structure was implemented to manage the drawing actions of the user. Various numerical analysis methods were also used to maintain the geometrical validity of patterns.

Details

International Journal of Clothing Science and Technology, vol. 33 no. 5
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 15 July 2022

Mehrnaz Ahmadi and Mehdi Khashei

The purpose of this paper is to propose a new linear-nonlinear data preprocessing-based hybrid model to achieve a more accurate result at a lower cost for wind power…

Abstract

Purpose

The purpose of this paper is to propose a new linear-nonlinear data preprocessing-based hybrid model to achieve a more accurate result at a lower cost for wind power forecasting. For this purpose, a decomposed based series-parallel hybrid model (PKF-ARIMA-FMLP) is proposed which can model linear/nonlinear and certain/uncertain patterns in underlying data simultaneously.

Design/methodology/approach

To design the proposed model at first, underlying data are divided into two categories of linear and nonlinear patterns by the proposed Kalman filter (PKF) technique. Then, the linear patterns are modeled by the linear-fuzzy nonlinear series (LLFN) hybrid models to detect linearity/nonlinearity and certainty/uncertainty in underlying data simultaneously. This step is also repeated for nonlinear decomposed patterns. Therefore, the nonlinear patterns are modeled by the linear-fuzzy nonlinear series (NLFN) hybrid models. Finally, the weight of each component (e.g. KF, LLFN and NLFN) is calculated by the least square algorithm, and then the results are combined in a parallel structure. Then the linear and nonlinear patterns are modeled with the lowest cost and the highest accuracy.

Findings

The effectiveness and predictive capability of the proposed model are examined and compared with its components, based models, single models, series component combination based hybrid models, parallel component combination based hybrid models and decomposed-based single model. Numerical results show that the proposed linear-nonlinear data preprocessing-based hybrid models have been able to improve the performance of single, hybrid and single decomposed based prediction methods by approximately 66.29%, 52.10% and 38.13% for predicting wind power time series in the test data, respectively.

Originality/value

The combination of single linear and nonlinear models has expanded due to the theory of the existence of linear and nonlinear patterns simultaneously in real-world data. The main idea of the linear and nonlinear hybridization method is to combine the benefits of these models to identify the linear and nonlinear patterns in the data in series, parallel or series-parallel based models by reducing the limitations of the single model that leads to higher accuracy, more comprehensiveness and less risky predictions. Although the literature shows that the combination of linear and nonlinear models can improve the prediction results by detecting most of the linear and nonlinear patterns in underlying data, the investigation of linear and nonlinear patterns before entering linear and nonlinear models can improve the performance, which in no paper this separation of patterns into two classes of linear and nonlinear is considered. So by this new data preprocessing based method, the modeling error can be reduced and higher accuracy can be achieved at a lower cost.

Details

Journal of Modelling in Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-5664

Keywords

Article
Publication date: 5 August 2022

Patrick Terrence Coyle and Roseanne Foti

The authors examine mutually exclusive sub-groups of congruent expectations for leader and follower roles relate to sub-groups of self-other endorsement, and how these…

Abstract

Purpose

The authors examine mutually exclusive sub-groups of congruent expectations for leader and follower roles relate to sub-groups of self-other endorsement, and how these patterns predict relationship quality.

Design/methodology/approach

The authors examine patterns of congruent implicit theories relate to patterns of self-other endorsement, at the dyadic level, using latent class analysis in 193 working-adult dyads. The authors then examine how these patterns predict leader and follower assessments of relationship quality using general linear models (GLM).

Findings

The authors supported 4 classes of dyads with specific patterns of congruent (or incongruent) ILT's and IFT's: Role congruent, exchange congruent, committed leader congruent, and role incongruent dyads. Class membership predicted leader-assessed leader-member exchange (LMX) and perceived support. The authors then supported 3 classes of self-other endorsement: dyads with mutual endorsement, leader identity endorsement, and no endorsement. Class membership predicted follower-assessed LMX, perceived support, and perceived contribution from leaders. Class membership corresponded meaningfully.

Originality/value

The authors empirically examine the extent to which relationship behavior can be understood: (1) by similar implicit theories, or (2) through identification with a leader or follower role. Moreover, the authors uncover unique combinations of congruence, and address a key challenge posed by traditional variable-oriented strategies typically used in LMX research.

Details

Leadership & Organization Development Journal, vol. 43 no. 6
Type: Research Article
ISSN: 0143-7739

Keywords

Article
Publication date: 1 March 2000

Tae J. Kang and Sung Min Kim

A comprehensive apparel CAD system was developed to perform automatic garment pattern drafting and the prediction of the final drape shape of designed garment putting on…

1970

Abstract

A comprehensive apparel CAD system was developed to perform automatic garment pattern drafting and the prediction of the final drape shape of designed garment putting on the human body. Three dimensional apparel CAD system starts with a flat garment pattern drafting system. A computerized pattern design script language has been created based on the traditional patterner’s principles to develop an automatic draft system of performing basic garment pattern drafting as well as grading rule generation. A pattern modification system was also developed considering functions required in apparel CAD such as auxiliary pattern generation, seam line creation, and dart manipulation to generate engineering patterns which can be used in the three dimensional garment shape prediction system presented later in part II of this paper.

Details

International Journal of Clothing Science and Technology, vol. 12 no. 1
Type: Research Article
ISSN: 0955-6222

Keywords

Article
Publication date: 1 October 2006

Alex Bennet and David Bennet

The purpose of this article is to link the associative learning process of the human brain to the relationship and emergence of really significant ideas on the global horizon.

Abstract

Purpose

The purpose of this article is to link the associative learning process of the human brain to the relationship and emergence of really significant ideas on the global horizon.

Design/methodology/approach

First, learning is explored from the viewpoint of the brain/mind, with a focus on the creation of patterns and their relationships to our personal frames of reference. Second, the associations of three really significant ideas are explored, and a pattern of patterns is surfaced.

Findings

The paper finds that in concert with the functioning of the brain, significant ideas emerge in relationship with other ideas that have personal historical significance, i.e. external patterns from the environment are detected, recognized, made sense of and have meaning in relationship with our internal patterns of significance.

Originality/value

The paper creates an appreciation of the role of patterns in thinking and learning.

Details

VINE, vol. 36 no. 4
Type: Research Article
ISSN: 0305-5728

Keywords

Book part
Publication date: 1 October 2008

Marvin L. Manheim

The field of transportation research, and the World Conferences on Transport Research Society (WCTRS), has an illustrious history. The challenge today is to look to the

Abstract

The field of transportation research, and the World Conferences on Transport Research Society (WCTRS), has an illustrious history. The challenge today is to look to the future in a deep and thoughtful way, to identify emerging issues and opportunities for practice and for research.

This chapter identifies several critical issues in transportation research. We then explore one issue in depth, the need to increase individual and collaborative effectiveness.

This issue points to the need for a new direction in transportation research. Historically, transportation research has focussed on two major thrusts. The analysis thrust has focussed on predicting the consequences of a given transportation plan, project, or service proposal. Associated “design” research has dealt with algorithmic and heuristic techniques for finding “good” plans or policies. The policy thrust has focussed on the organizational issues and processes around making decisions on and implementing transportation plans, projects, or services. This thrust includes public policy issues and approaches, market structures and regulatory processes, organization design issues, distributional consequences, environmental policy issues, and related areas.

Today, we see the need to add a third major thrust to the field of transportation — cognition and action:

  • to manage transportation services and enterprises better;

  • to use analysis more effectively to inform and influence decisions;

  • to use computer support more effectively in transportation organizations and enterprises.

to manage transportation services and enterprises better;

to use analysis more effectively to inform and influence decisions;

to use computer support more effectively in transportation organizations and enterprises.

Research in cognition and action examines how people think and act, and aims to develop aids to thinking and acting that result in significant improvements in peoples' behaviours and performance. Promising research directions draw on cognitive psychology and cognitive science, and especially empirical research on managerial cognition and problem-solving. We introduce the theory of cognitive informatics, describe some applications to transportation management, and discuss relevant software tools.

Details

Recent Developments in Transport Modelling
Type: Book
ISBN: 978-0-08-045119-0

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