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Article
Publication date: 1 February 2002

K. Murugesan, H.R. Thomas and P.J. Cleall

A numerical study is carried out to investigate the influence of multistage drying regimes on the drying kinematics of a porous material. In particular the effects of varying the…

555

Abstract

A numerical study is carried out to investigate the influence of multistage drying regimes on the drying kinematics of a porous material. In particular the effects of varying the conditions of the drying medium are studied. The drying model for the solid is developed based on the continuum approach. A series of simulations of the drying behaviour of a rectangular brick with varying temperature, heat transfer coefficient and relative humidity of the drying medium are undertaken. It is found that the total drying time is mainly dependent on the relative humidity of the drying medium. Also condensation is predicted on the surface of the brick, with the quantity of condensation being directly linked to the relative humidity and temperature of the drying medium. Overall it is concluded that multistage drying regimes are useful in reducing the overall drying time whilst avoiding detrimental shrinkage during the constant drying period.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 12 no. 1
Type: Research Article
ISSN: 0961-5539

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Article
Publication date: 8 June 2021

Ratnadeep Nath and Krishnan Murugesan

This study aims to investigate the buoyancy-induced heat and mass transfer phenomena in a backward-facing-step (BFS) channel subjected to applied magnetic field using different…

242

Abstract

Purpose

This study aims to investigate the buoyancy-induced heat and mass transfer phenomena in a backward-facing-step (BFS) channel subjected to applied magnetic field using different types of nanofluid.

Design/methodology/approach

Conservation equations of mass, momentum, energy and concentration are used through velocity-vorticity form of Navier–Stokes equations and solved using Galerkin’s weighted residual finite element method. The density variation is handled by Boussinesq approximation caused by thermo-solutal buoyancy forces evolved at the channel bottom wall having high heat and concentration. Simulations were carried out for the variation of Hartmann number (0 to 100), buoyancy ratio (−10 to +10), three types of water-based nanofluid i.e. Fe3O4, Cu, Al2O3 at χ = 6%, Re = 200 and Ri = 0.1.

Findings

The mutual interaction of magnetic force, inertial force and nature of thermal-solutal buoyancy forces play a significant role in the heat and mass transport phenomena. Results show that the size of the recirculation zone increases at N = 1 for aiding thermo-solutal buoyancy force, whereas the applied magnetic field dampened the fluid-convection process. With an increase in buoyancy ratio, Al2O3 nanoparticle shows a maximum 54% and 67% increase in convective heat and mass transfer, respectively at Ha = 20 followed by Fe3O4 and Cu. However, with increase in Ha the Nuavg and Shavg diminish by maximum 62.33% and 74.56%, respectively, for Fe3O4 nanoparticles at N = 5 followed by Al2O3 and Cu.

Originality/value

This research study numerically examines the sensitivity of Fe3O4, Cu and Al2O3 nanoparticles in a magnetic field for buoyancy-induced mixed convective heat and mass transfer phenomena in a BFS channel, which was not analyzed earlier.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 32 no. 3
Type: Research Article
ISSN: 0961-5539

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Article
Publication date: 6 June 2024

Ömür Kıvanç Kürkçü and Mehmet Sezer

This study aims to treat a novel system of Volterra integro-differential equations with multiple delays and variable bounds, constituting a generic numerical method based on the…

59

Abstract

Purpose

This study aims to treat a novel system of Volterra integro-differential equations with multiple delays and variable bounds, constituting a generic numerical method based on the matrix equation and a combinatoric-parametric Charlier polynomials. The proposed method utilizes these polynomials for the matrix relations at the collocation points.

Design/methodology/approach

Thanks to the combinatorial eligibility of the method, the functional terms can be transformed into the generic matrix relations with low dimensions, and their resulting matrix equation. The obtained solutions are tested with regard to the parametric behaviour of the polynomials with $\alpha$, taking into account the condition number of an outcome matrix of the method. Residual error estimation improves those solutions without using any external method. A calculation of the residual error bound is also fulfilled.

Findings

All computations are carried out by a special programming module. The accuracy and productivity of the method are scrutinized via numerical and graphical results. Based on the discussions, one can point out that the method is very proper to solve a system in question.

Originality/value

This paper introduces a generic computational numerical method containing the matrix expansions of the combinatoric Charlier polynomials, in order to treat the system of Volterra integro-differential equations with multiple delays and variable bounds. Thus, the method enables to evaluate stiff differential and integral parts of the system in question. That is, these parts generates two novel components in terms of unknown terms with both differentiated and delay arguments. A rigorous error analysis is deployed via the residual function. Four benchmark problems are solved and interpreted. Their graphical and numerical results validate accuracy and efficiency of the proposed method. In fact, a generic method is, thereby, provided into the literature.

Details

Engineering Computations, vol. 41 no. 4
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 16 December 2022

Arunodaya Raj Mishra, Pratibha Rani, Abhijit Saha, Dragan Pamucar and Ibrahim M. Hezam

Reverse logistics (RL) is a type of supply chain management that moves goods from the end customer to the original manufacturer for reuse, remanufacturing and disposal purposes…

265

Abstract

Purpose

Reverse logistics (RL) is a type of supply chain management that moves goods from the end customer to the original manufacturer for reuse, remanufacturing and disposal purposes. Owing to growing environmental legislations and the development of new technologies in marketing, RL has attracted more significance among experts and academicians. Outsourcing RL practices to third-party reverse logistics provider (3PRLP) has been identified as one of the most important management strategies due to complexity of RL operations and the lack of available resource. Current sustainability trends have made 3PRLP assessment and selection process more complex. In order to select the 3PRLP, the existence of several aspects of sustainability motivates the experts to establish a new multi-criteria decision analysis (MCDA) approach.

Design/methodology/approach

With the growing complexity and high uncertainty of decision environments, the preference values of 3PRLPs are not always expressed with real numbers. As the generalized version of fuzzy set, intuitionistic fuzzy set and Fermatean fuzzy set, the theory of q-rung orthopair fuzzy set (q-ROFS) is used to permit decision experts (DEs) to their assessments in a larger space and to better cope with uncertain information. Given that the combined compromise solution (CoCoSo) is an innovative MCDA approach with higher degree of stability and reliability than several existing methods.

Findings

To exhibit the potentiality and applicability of the presented framework, a case study of S3PRLPs assessment is taken from q-rung orthopair fuzzy perspective. The assessment process consists of three sustainability aspects namely economic, environment and social dimensions related with a total of 14 criteria. Further, sensitivity and comparative analyses are made to display the solidity and strength of the presented approach. The results of this study approve that the presented methodology is more stable and efficient in comparison with other methods.

Originality/value

Thus, the objective of the study is to develop a hybrid decision-making methodology by combining CoCoSo method and discrimination measure with q-ROFS for selecting an appropriate sustainable 3PRLP (S3PRLP) candidate under uncertain environment. In the proposed method, a novel procedure is proposed to obtain the weights of DEs within q-ROFS context. To calculate the criteria weights, a new formula is presented based on discrimination measure, which provides more realistic weights. In this respect, a new discrimination measure is proposed for q-ROFSs.

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Article
Publication date: 14 May 2021

Lijuan Chen, Ditao Duan, Arunodaya Raj Mishra and Melfi Alrasheedi

This study caries a survey approach using the expert's interview and literature to select the important criteria to select and evaluate the third-party reverse logistics providers…

822

Abstract

Purpose

This study caries a survey approach using the expert's interview and literature to select the important criteria to select and evaluate the third-party reverse logistics providers (3PRLPs) in manufacturing companies. In total, 16 criteria are selected to evaluate 3PRLPs, and these criteria are classified on the basis of three main elements of sustainable growth, including economic, social and environmental development. Therefore, a hybrid decision-making approach is utilized to evaluate and rank the 3PRLPs in manufacturing companies.

Design/methodology/approach

This paper proposes a new decision-making approach using the projection model and entropy method under the interval-valued intuitionistic fuzzy set to assess 3PRLPs based on sustainability perspectives. A survey approach using the literature review and experts' interview is conducted to select the important criteria to select and evaluate 3PRLPs in manufacturing companies. To assess the criteria weight, the entropy method is used. Further, the projection model is applied to prioritize the 3PRLPs option. Sensitivity analysis and comparison process are performed in order to test and validate the developed method.

Findings

The presented methodology uses the benefits to determine the former for measuring the parameters considered and the latter for rating the 3PRLPs alternatives. A case study is taken to 3PRLPs in the manufacturing industry to illustrate the efficiency of the introduced hybrid method. The findings of this study indicate that when facing uncertainties of input and qualitative data, the proposed solution delivers more viable performance and therefore is suitable for wider uses.

Originality/value

The conception of the circular economy (CE) comes from the last 4 decades, and in recent years, tremendous attention has been carried out on this concept, partially because of the availability of natural resources in the world and changes in consumption behaviour of developed and developing nations. Remarkably, the sustainable supply chain management concepts are established parallel to the CE foundations, grown in industrial practice and ecology literature for a long time. In fact, to reduce the environmental concerns, sustainable supply chain management seeks to diminish the materials' flow and minimize the unintentional harmful consequences of consumption and production processes. Customers and governments are becoming increasingly aware of the environmental sustainability in the CE era, which allows businesses to concentrate more resources on reverse logistics (RLs). However, most manufacturing enterprises have been inspired to outsource their RL operations to competent 3PRLPs due to limited resources and technological limitations. In RL outsourcing practices, the selection of the best 3PRLP is helpfully valuable due to its potential to increase the economic viability of enterprises and boost their long-term growth.

Details

Journal of Enterprise Information Management, vol. 35 no. 4/5
Type: Research Article
ISSN: 1741-0398

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Book part
Publication date: 10 February 2023

Laxmi Pandit Vishwakarma and Rajesh Kumar Singh

Introduction: Artificial intelligence (AI) is being extensively used to solve complex problems in the industry. AI provides several benefits such as providing visibility in the…

Abstract

Introduction: Artificial intelligence (AI) is being extensively used to solve complex problems in the industry. AI provides several benefits such as providing visibility in the processes, reducing time, improving accuracy, saving time, helping in the decision-making process, etc. Due to the range of benefits of AI technologies, organisations readily adopt this technology. However, there are several challenges that the organisation faces during the implementation of AI. These challenges are in context to human resource (HR) development for successful implementation of AI across different functions and are discussed in this chapter.

Purpose: Although we know that AI technology is widely accepted in human resource management (HRM) due to its various benefits. But the organisations face many challenges during the implementation of AI. The focus of the study is to explore the literature on AI in HRM, identify the challenges of implementing AI and provide potential future research direction based on a systematic literature review.

Methodology: To explore the literature on AI in HRM, the study undertakes a systematic literature review. The study identifies, analyse and classifies the literature to provide a holistic view of HR challenges in implementing AI. The study is built on a review of 47 documents, including the articles, book chapters and conference papers using the Scopus database for the past 10 years (2012–27 January 2022).

Findings: The study provides an overview of the documents published in Scopus in this area through a systematic literature review. The study reveals that a significant amount of growth in the publication has been shown in the past 10 years. The maximum and continuous growth is shown after 2017. The maximum number of papers are published in India, the USA and China. The study identifies major eight challenges of AI implementation in HRM. The study also provides a secondary case to deep dive in this area based on a systematic literature review.

Research Limitation/Implication: The challenges identified in the study are not empirically tested. Each of the identified challenges should be empirically examined. This study has expanded the body of knowledge of AI in HRM. This study will help the academicians and practitioners work on the identified challenges and help the organisations ease in adopting AI.

Originality/Value: This study represents the first work that integrates AI implementation challenges in HRM.

Details

The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part B
Type: Book
ISBN: 978-1-80455-662-7

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Article
Publication date: 5 June 2012

A. Rahim, U. Sharma, K. Murugesan, A. Sharma and P. Arora

This paper presents results of an experimental study undertaken to optimize the residual compressive strength of heated concrete with respect to various mix design parameters…

66

Abstract

This paper presents results of an experimental study undertaken to optimize the residual compressive strength of heated concrete with respect to various mix design parameters using the Taguchi method. The design of experiments (DoE) was carried out by standard L9 (34) orthogonal array (OA) of four factors with three material parameter levels. The factors considered were water-cement ratio, cement content, super-plasticizer dosage and fine aggregate content. The specimens were heated up to 200°C, 400°C, 600°C and 800°C target temperatures and were subsequently tested under axial compressive loads in cooled condition. Based on the results, the material parameter responses of optimum performance characteristics were analyzed by statistical analysis of signal to noise ratio (S/N) and analysis of variance (ANOVA) techniques to maximize the post-fire residual compressive strength of concrete. The results indicate that the best level of control factors paid their own contribution of compressive strength at various elevated temperatures. The confirmation tests corroborated the theoretical optimum test conditions.

Details

Journal of Structural Fire Engineering, vol. 3 no. 2
Type: Research Article
ISSN: 2040-2317

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Abstract

Details

Knowledge Management for Leadership and Communication
Type: Book
ISBN: 978-1-83982-045-8

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Article
Publication date: 2 April 2024

R.S. Vignesh and M. Monica Subashini

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories…

40

Abstract

Purpose

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

Design/methodology/approach

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

Findings

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

Originality/value

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

Available. Content available

Abstract

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 32 no. 5
Type: Research Article
ISSN: 0961-5539

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