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Article
Publication date: 18 January 2024

Kajal Vinayak and Shripad P. Mahulikar

In recent years, increased use of all-aspect infrared (IR)-guided missiles based on the long-wave infrared (LWIR; 8–12 µm) band has lowered the probability of aircraft survival in…

Abstract

Purpose

In recent years, increased use of all-aspect infrared (IR)-guided missiles based on the long-wave infrared (LWIR; 8–12 µm) band has lowered the probability of aircraft survival in warfare. The lock-on of these highly sensitive missiles is difficult to break, especially from the front. Aerodynamically heated swept-back leading edges (SBLE), because of their high temperature and large area, serve as a prominent LWIR source for aircraft detection from the front. This study aims to report the influence of sweep-back angle (Λ, based on the Mach number [M]) on aerodynamic heating and the LWIR signature of SBLE.

Design/methodology/approach

The temperature along SBLE is obtained numerically as radiation equilibrium temperature (Tw) by discretizing the SBLE length into “n” number of segments, and for each segment, emission based on Tw is evaluated. IR radiance due to reflected external sources (sky-shine and Earthshine) and radiance due to Tw are collectively used to determine the IR contrast between SBLE and its replaced background in the LWIR band (icont-SBLE,LWIR).

Findings

The results are obtained for low subsonic turboprop aircraft (Λ = 3°, M = 0.44); high subsonic strategic bombers (Λ = 35°, M = 0.8); fifth-generation stealth aircraft (Λ = 40°, M = 1.6); and aircraft with supercruise/supersonic capability (Λ = 50°, M = 2.5). The aircraft with supersonic capability (Λ = 50°, M = 2.5) reports the maximum LWIR signatures and hence the highest visibility from the front. The results obtained are compared with values at Λ = 0° for all cases, which shows that increasing Λ significantly reduces aerodynamic heating and LWIR signatures.

Originality/value

The novelty of this study comes from its report on the influence of Λ on the LWIR signatures of aircraft SBLE in the frontal aspect for the first time.

Details

Aircraft Engineering and Aerospace Technology, vol. 96 no. 2
Type: Research Article
ISSN: 1748-8842

Keywords

Open Access
Article
Publication date: 12 January 2024

B.S. Patil and M.R. Suji Raga Priya

The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components…

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Abstract

Purpose

The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.

Design/methodology/approach

A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.

Findings

Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.

Research limitations/implications

Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.

Originality/value

Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.

Details

Vilakshan - XIMB Journal of Management, vol. 21 no. 1
Type: Research Article
ISSN: 0973-1954

Keywords

Article
Publication date: 26 January 2024

Mohsen Rajabzadeh, Seyed Meysam Mousavi and Farzad Azimi

This paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers…

Abstract

Purpose

This paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.

Design/methodology/approach

A new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.

Findings

According to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.

Originality/value

An integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores’ desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

Details

Kybernetes, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 22 March 2024

Mohd Mustaqeem, Suhel Mustajab and Mahfooz Alam

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have…

Abstract

Purpose

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

Design/methodology/approach

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

Findings

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Originality/value

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

Details

International Journal of Intelligent Computing and Cybernetics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1756-378X

Keywords

Book part
Publication date: 14 March 2024

Chelsea Phillips, Marc Becker, Gaby Odekerken-Schröder and Dominik Mahr

Service robots present a new frontier in the provision of services, with far-reaching implications for customers and managers alike. The purpose of this chapter is to examine how…

Abstract

Service robots present a new frontier in the provision of services, with far-reaching implications for customers and managers alike. The purpose of this chapter is to examine how service robots impact service providers' current marketing strategies. For this, the authors perform an integrative, nonsystematic review of international gray and academic literature to understand how both practitioners and academics perceive the impacts of the technology. Based on this analysis, the present work identifies three key themes that emerge from the current state of practitioner and academic research, namely (1) service robots demand new core business capabilities and competencies, (2) service robots offer new value propositions, and (3) service robots impact not only service providers' cost structures but also revenue streams. These insights are combined into the Service Robot Innovation Canvas, a visual tool for service providers to identify the impact of service robot implementations on a company's marketing strategy. In addition, based on the analyzed literature, the most pressing questions for researchers are laid out in a research agenda.

Details

The Impact of Digitalization on Current Marketing Strategies
Type: Book
ISBN: 978-1-83753-686-3

Keywords

Article
Publication date: 15 September 2023

Tooraj Karimi and Mohamad Ahmadian

Competition in the banking sector is more complex than in the past, and survival has become more difficult than before. The purpose of this paper is to propose a grey methodology…

Abstract

Purpose

Competition in the banking sector is more complex than in the past, and survival has become more difficult than before. The purpose of this paper is to propose a grey methodology for evaluating, clustering and ranking the performance of bank branches with imprecise and uncertain data in order to determine the relative status of each branch.

Design/methodology/approach

In this study, the two-stage data envelopment analysis model with grey data is applied to assess the efficiency of bank branches in terms of operations. The result of grey two-stage data envelopment analysis model is a grey number as efficiency value of each branch. In the following, the branches are classified into three grey categories of performance by grey clustering method, and the complete grey ranking of branches are performed using “minimax regret-based approach” and “whitening value rating”.

Findings

The results show that after grey clustering of 22 branches based on grey efficiency value obtained from the grey two-stage DEA model, 6 branches are assigned to “excellent” class, 4 branches to “good” class and 12 branches to “poor” class. Moreover, the results of MRA and whitening value rating models are integrated, and a complete ranking of 22 branches are presented.

Practical implications

Grey clustering of branches based on grey efficiency value can facilitate planning and policy-making for branches so that there is no need to plan separately for each branch. The grey ranking helps the branches find their current position compared to other branches, and the results can be a dashboard to find the best practices for benchmarking.

Originality/value

Compared with traditional DEA methods which use deterministic data and consider decision-making units as black boxes, in this research, a grey two-stage DEA model is proposed to evaluate the efficiency of bank branches. Furthermore, grey clustering and grey ranking of efficiency values are used as a novel solution for improving the accuracy of grey two-stage DEA results.

Details

Grey Systems: Theory and Application, vol. 14 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 6 October 2023

Jie Yang, Manman Zhang, Linjian Shangguan and Jinfa Shi

The possibility function-based grey clustering model has evolved into a complete approach for dealing with uncertainty evaluation problems. Existing models still have problems…

Abstract

Purpose

The possibility function-based grey clustering model has evolved into a complete approach for dealing with uncertainty evaluation problems. Existing models still have problems with the choice dilemma of the maximum criteria and instances when the possibility function may not accurately capture the data's randomness. This study aims to propose a multi-stage skewed grey cloud clustering model that blends grey and randomness to overcome these problems.

Design/methodology/approach

First, the skewed grey cloud possibility (SGCP) function is defined, and its digital characteristics demonstrate that a normal cloud is a particular instance of a skewed cloud. Second, the border of the decision paradox of the maximum criterion is established. Third, using the skewed grey cloud kernel weight (SGCKW) transformation as a tool, the multi-stage skewed grey cloud clustering coefficient (SGCCC) vector is calculated and research items are clustered according to this multi-stage SGCCC vector with overall features. Finally, the multi-stage skewed grey cloud clustering model's solution steps are then provided.

Findings

The results of applying the model to the assessment of college students' capacity for innovation and entrepreneurship revealed that, in comparison to the traditional grey clustering model and the two-stage grey cloud clustering evaluation model, the proposed model's clustering results have higher identification and stability, which partially resolves the decision paradox of the maximum criterion.

Originality/value

Compared with current models, the proposed model in this study can dynamically depict the clustering process through multi-stage clustering, ensuring the stability and integrity of the clustering results and advancing grey system theory.

Details

Grey Systems: Theory and Application, vol. 14 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 28 November 2023

Huan Wang, Daao Wang, Peng Wang and Zhigeng Fang

The purpose of this research is to provide a theoretical framework for complex equipment quality risk evaluation. The primary aim of the framework is to enhance the ability to…

Abstract

Purpose

The purpose of this research is to provide a theoretical framework for complex equipment quality risk evaluation. The primary aim of the framework is to enhance the ability to identify risks and improve risk control efficiency during the development phase.

Design/methodology/approach

A novel framework for quality risk evaluation in complex equipment is proposed, which integrates probabilistic hesitant fuzzy set-quality function deployment (PHFS-QFD) and grey clustering. PHFS-QFD is applied to identify the quality risk factors, and grey clustering is used to evaluate quality risks in cases of poor quality information during the development stage. The unfolding function of QFD is applied to simplify complex evaluation problems.

Findings

The methodology presents an innovative approach to quality risk evaluation for complex equipment development. The case analysis demonstrates that this method can efficiently evaluate the quality risks for aircraft development and systematically trace back the risk factors through hierarchical relationships. In comparison to traditional failure mode and effects analysis methods for quality risk assessment, this approach exhibits superior effectiveness and reliability in managing quality risks for complex equipment development.

Originality/value

This study contributes to the field by introducing a novel theoretical framework that combines PHFS-QFD and grey clustering. The integration of these approaches significantly improves the quality risk evaluation process for complex equipment development, overcoming challenges related to data scarcity and simplifying the assessment of intricate systems.

Details

Grey Systems: Theory and Application, vol. 14 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 18 August 2023

Qinggang Shi, Peng Li and Zhiwei Xu

The purpose of this paper is to propose a consensus method for multi-attribute group decision-making (MAGDM) problems based on preference-approval structure and regret theory…

Abstract

Purpose

The purpose of this paper is to propose a consensus method for multi-attribute group decision-making (MAGDM) problems based on preference-approval structure and regret theory, which can improve the efficiency of decision-making and promote the consensus level among individuals.

Design/methodology/approach

First, a new method to obtain the reference points based on regret theory and expert weighting method is proposed. Second, a consensus reaching method based on preference-approval structure is proposed. Then, an adjustment mechanism to further improve the consensus level between individuals is designed. Finally, an example of the assessment of elderly care institutions is used to illustrate the feasibility and effectiveness of the proposed method.

Findings

The feasibility and validity of the proposed method are verified by comparing with the advanced two-stage minimum adjustment method. The compared results show that the proposed method is more consistent with the actual situation.

Research limitations/implications

This paper presents a consensus reaching method for MAGDM based on preference-approval structure, which considers the avoidance behaviors of individuals and reference points. Decision makers (DMs) can use this approach to rank and categorize alternatives while further increasing the level of consensus among them. This can further help determine the optimal alternative more efficiently.

Originality/value

A new MAGDM problem based on the combination of regret theory and individual reference points is proposed. Besides, a new method of obtaining experts' weights and a consensus reaching method for MAGDM based on preference-approval structure are designed.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 17 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

Open Access
Article
Publication date: 28 June 2022

Yahya Alnashri and Hasan Alzubaidi

The main purpose of this paper is to introduce the gradient discretisation method (GDM) to a system of reaction diffusion equations subject to non-homogeneous Dirichlet boundary…

Abstract

Purpose

The main purpose of this paper is to introduce the gradient discretisation method (GDM) to a system of reaction diffusion equations subject to non-homogeneous Dirichlet boundary conditions. Then, the authors show that the GDM provides a comprehensive convergence analysis of several numerical methods for the considered model. The convergence is established without non-physical regularity assumptions on the solutions.

Design/methodology/approach

In this paper, the authors use the GDM to discretise a system of reaction diffusion equations with non-homogeneous Dirichlet boundary conditions.

Findings

The authors provide a generic convergence analysis of a system of reaction diffusion equations. The authors introduce a specific example of numerical scheme that fits in the gradient discretisation method. The authors conduct a numerical test to measure the efficiency of the proposed method.

Originality/value

This work provides a unified convergence analysis of several numerical methods for a system of reaction diffusion equations. The generic convergence is proved under the classical assumptions on the solutions.

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