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1 – 10 of 498
Article
Publication date: 14 August 2023

Monika Sheoran and Devashish Das Gupta

India generates around two million tonnes of e-waste every year, and it is increasing at a very high rate of 30%. However, due to inefficient handling of infrastructure and…

Abstract

Purpose

India generates around two million tonnes of e-waste every year, and it is increasing at a very high rate of 30%. However, due to inefficient handling of infrastructure and limited number of collection centres along with the absence of proper incentive structure for producer and recyclers, 95% of e-waste reaches to unorganized sector for disposal. Consumers are not aware of the need of proper e-waste disposal and in absence of proper motivation and they are not inclined towards recycling process. Therefore, this paper aims to identify the best practices of e-waste take adopted all over the world to implement effective policy interventions for e-waste management in India and other emerging economies.

Design/methodology/approach

This paper has recommended preventive as well as curative policy interventions on the basis of best e-waste management practices of Germany, Italy and Japan; life cycle assessment of e-waste; and SWOT analysis of Indian electronic product industry.

Findings

Preventive measures include a deposit refund scheme wherein a consumer will be responsible for depositing a refundable fees during the purchase of the product. The amount should be arrived at keeping in mind cost involved in handling e-waste and ensure some motivation for the consumers to give back used product. To ensure proper tracking of the product, Radio frequency identification (RFID) tags can be used which will be activated at the time of sale of product and remain so until product reaches some designated recycling space or recycler and consumer is returned back his deposit fee. Subsidy to the producers and recyclers can also be provided by the government to further incentivize the whole process. An example of mobile phones has been used to understand the proposed deposit fees and associated cost structure. Curative measures to reduce the generation of e-waste in long run for managing the discussed issue have also been proposed.

Originality/value

This study is an initiative for proposing and implementing best e-waste take back techniques in a developing economy like India by acquiring learnings from best/advanced economies in terms of e-waste take back.

Open Access
Article
Publication date: 27 March 2023

Musa Nyathi and Ray Kekwaletswe

The purpose of this paper is to examine whether employee outcomes of employee performance and job satisfaction mediate and enhance the effect of e-HRM usage on organizational…

4361

Abstract

Purpose

The purpose of this paper is to examine whether employee outcomes of employee performance and job satisfaction mediate and enhance the effect of e-HRM usage on organizational performance.

Design/methodology/approach

Data were collected through a survey involving 35 organizations using e-HRM systems. A partially mixed sequential dominant status explanatory design was used for the study. A stratified convenience sampling technique was used for the quantitative phase of the study. A purposive sampling technique was employed for the qualitative phase. A structural equation modelling technique with the use of the process macro approach was used to analyse collected data.

Findings

There is a positive relationship between e-HRM usage and employee outcomes. Employee performance and job satisfaction mediate the effect of e-HRM usage on organizational performance. Employee performance and job satisfaction are contextual variables that characterize effective e-HRM configurations.

Practical implications

Organizations should invest in employee outcomes in order to maximize the potential of e-HRM. The e-HRM configurations characterized by a multiplicity of dimensions are more likely to add to organizational value creation. The deployment of e-HRM systems should be preceded by high levels of employee performance and job satisfaction, for organizational success.

Originality/value

The study contributes to a growing body of knowledge on dimensions, which characterize effective e-HRM configurations, yielding organizational success. Employee performance and job satisfaction should be added to the characteristics of effective e-HRM configurations.

Details

Journal of Organizational Effectiveness: People and Performance, vol. 11 no. 1
Type: Research Article
ISSN: 2051-6614

Keywords

Article
Publication date: 9 February 2024

Rizk Mostafa Shalaby and Mohamed Saad

The purpose of the present work is to study the impacts of rapid cooling and Tb rare-earth additions on the structural, thermal and mechanical behavior of Bi–0.5Ag lead-free…

Abstract

Purpose

The purpose of the present work is to study the impacts of rapid cooling and Tb rare-earth additions on the structural, thermal and mechanical behavior of Bi–0.5Ag lead-free solder for high-temperature applications.

Design/methodology/approach

Effect of rapid solidification processing on structural, thermal and mechanical properties of Bi-Ag lead-free solder reinforced Tb rare-earth element.

Findings

The obtained results indicated that the microstructure consists of rhombohedral Bi-rich phase and Ag99.5Bi0.5 intermetallic compound (IMC). The addition of Tb could effectively reduce the onset and melting point. The elastic modulus of Tb-containing solders was enhanced to about 90% at 0.5 Tb. The higher elastic modulus may be attributed to solid solution strengthening effect, solubility extension, microstructure refinement and precipitation hardening of uniform distribution Ag99.5Bi0.5 IMC particles which can reasonably modify the microstructure, as well as inhibit the segregation and hinder the motion of dislocations.

Originality/value

It is recommended that the lead-free Bi-0.5Ag-0.5Tb solder be a candidate instead of common solder alloy (Sn-37Pb) for high temperature and high performance applications.

Details

Soldering & Surface Mount Technology, vol. 36 no. 2
Type: Research Article
ISSN: 0954-0911

Keywords

Article
Publication date: 11 May 2023

Helen Crompton, Mildred V. Jones, Yaser Sendi, Maram Aizaz, Katherina Nako, Ricardo Randall and Eric Weisel

The purpose of this study is to determine what technological strategies were used within each of the phases of the ADDIE framework when developing content for professional…

554

Abstract

Purpose

The purpose of this study is to determine what technological strategies were used within each of the phases of the ADDIE framework when developing content for professional training. The study also examined the affordances of those technologies in training.

Design/methodology/approach

A PRISMA systematic review methodology (Moher et al., 2015) was utilized to answer the four questions guiding this study. Specifically, the PRISMA extension Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Protocols (PRISMA-P, Moher et al., 2015) was used to direct each stage of the research, from the literature review to the conclusion. In addition, the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA principles; Liberati et al., 2009) are used to guide the article selection process.

Findings

The findings reveal that the majority of the studies were in healthcare (36%) and education (24%) and used an online format (65%). There was a wide distribution of ADDIE used with technology across the globe. The coding for the benefits of technology use in the development of the training solution revealed four trends: 1) usability, 2) learning approaches, 3) learner experience and 4) financial.

Research limitations/implications

This systematic review only examined articles published in English, which may bias the findings to a Western understanding of how technology is used within the ADDIE framework. Furthermore, the study examined only peer-review academic articles from scholarly journals and conferences. While this provided a high level of assurance about the quality of the studies, it does not include other reports directly from training providers and other organizations.

Practical implications

These findings can be used as a springboard for training providers, scholars, funders and practitioners, providing rigorous insight into how technology has been used within the ADDIE framework, the types of technology, and the benefits of using technology. This insight can be used when designing future training solutions with a better understanding of how technology can support learning.

Social implications

This study provides insight into the uses of technology in training. Many of these findings and uses of technology within ADDIE can also transfer to other aspects of society.

Originality/value

This study is unique in that it provides the scholarly community with the first systematic review to examine what technological strategies were used within each of the phases of the ADDIE structure and how these technologies provided benefits to developing a training solution.

Details

European Journal of Training and Development, vol. 48 no. 3/4
Type: Research Article
ISSN: 2046-9012

Keywords

Open Access
Article
Publication date: 23 January 2024

Luís Jacques de Sousa, João Poças Martins, Luís Sanhudo and João Santos Baptista

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase…

Abstract

Purpose

This study aims to review recent advances towards the implementation of ANN and NLP applications during the budgeting phase of the construction process. During this phase, construction companies must assess the scope of each task and map the client’s expectations to an internal database of tasks, resources and costs. Quantity surveyors carry out this assessment manually with little to no computer aid, within very austere time constraints, even though these results determine the company’s bid quality and are contractually binding.

Design/methodology/approach

This paper seeks to compile applications of machine learning (ML) and natural language processing in the architectural engineering and construction sector to find which methodologies can assist this assessment. The paper carries out a systematic literature review, following the preferred reporting items for systematic reviews and meta-analyses guidelines, to survey the main scientific contributions within the topic of text classification (TC) for budgeting in construction.

Findings

This work concludes that it is necessary to develop data sets that represent the variety of tasks in construction, achieve higher accuracy algorithms, widen the scope of their application and reduce the need for expert validation of the results. Although full automation is not within reach in the short term, TC algorithms can provide helpful support tools.

Originality/value

Given the increasing interest in ML for construction and recent developments, the findings disclosed in this paper contribute to the body of knowledge, provide a more automated perspective on budgeting in construction and break ground for further implementation of text-based ML in budgeting for construction.

Details

Construction Innovation , vol. 24 no. 7
Type: Research Article
ISSN: 1471-4175

Keywords

Article
Publication date: 15 March 2024

Jiantao Zhu, Chuhan Cao, Hefu Liu, Eric Tze Kuan Lim and Chee-Wee Tan

Research on electric sports (eSports) has experienced significant growth in recent years as a consequence of increasing connectivity, institutionalization, and technological…

52

Abstract

Purpose

Research on electric sports (eSports) has experienced significant growth in recent years as a consequence of increasing connectivity, institutionalization, and technological advances. However, the interdisciplinary nature of the eSports as a field and the burgeoning growth in eSports articles have rendered it necessary to conduct a systematic review of extant literature to take stock of the knowledge accumulated. To this end, we aim to undertake a comprehensive review of extant literature that takes stock of published research to derive opportunities for future research in the realm of eSports. In so doing, we contribute to the advancement of the field by mapping out the knowledge trajectory of eSports research and elucidating areas that have remained underexplored thus far.

Design/methodology/approach

To conduct systematic review of the eSports literature, we employed a framework that included six essential steps: protocol, search, appraisal, synthesis, analysis, and report. This comprehensive approach enables us to meticulously investigate the existing body of literature on eSports and identify key trends and topics addressed within the field. By conducting the multidisciplinary systematic literature review, we thoroughly assess the current state of eSports literature and subsequently outline potential research avenues that can contribute to eSports fields.

Findings

This study draws on a six-phase framework – member preparation, team formation, character selection, team coordination, team performance, and team reflection – to illustrate the roles played by different levels of analysis unit (i.e. characters, players, and teams) and three distinct yet interconnected stages (i.e. inputs, process, and outputs) within eSports games as well as the research opportunities it brings.

Originality/value

We conducted a rigorous systematic review of the eSports literature by using quantitative citation analysis and qualitative content analysis. Furthermore, we adopted team dynamic view of eSports to identify potential research avenues for future research that contribute to advancing our understanding of the eSports tournaments.

Details

Industrial Management & Data Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 22 January 2024

Cong Liu, Yanguo Yin and Rongrong Li

This study aims to investigate the effects of ball–material ratio on the properties of mixed powders and Cu-Bi self-lubricating alloy materials.

Abstract

Purpose

This study aims to investigate the effects of ball–material ratio on the properties of mixed powders and Cu-Bi self-lubricating alloy materials.

Design/methodology/approach

Cu-Bi mixed powder was ball milled at different ball–material ratios, and the preparation of Cu-Bi alloy materials was achieved through powder metallurgy technology. Scanning electron microscopy, X-ray diffraction and Raman spectroscopy were conducted to study the microstructure and phase composition of the mixed powder. The apparent density and flow characteristics of mixed powders were investigated using a Hall flowmeter. Tests on the crushing strength, impact toughness and tribological properties of self-lubricating alloy materials were conducted using a universal electronic testing machine, 300 J pendulum impact testing machine and M200 ring-block tribometer, respectively.

Findings

With the increase in ball–material ratio, the spherical copper matrix particles in the mixed powder became lamellar, the mechanical properties of the material gradually reduced, the friction coefficient of the material first decreased and then stabilized and the wear rate decreased initially and then increased. The increase in the ball–material ratio resulted in the fine network distribution of the Bi phase in the copper alloy matrix, which benefitted its enrichment on the worn surface for the formation a lubricating film and improvement of the material’s tribological performance. However, a large ball–material ratio can excessively weaken the mechanical properties of the material and reduce its wear resistance.

Originality/value

The effects of ball–material ratio on Cu-Bi mixed powder and material properties were clarified. This work provides a reference for the mechanical alloying process and its engineering applications.

Details

Industrial Lubrication and Tribology, vol. 76 no. 2
Type: Research Article
ISSN: 0036-8792

Keywords

Open Access
Article
Publication date: 1 March 2024

Quoc Duy Nam Nguyen, Hoang Viet Anh Le, Tadashi Nakano and Thi Hong Tran

In the wine industry, maintaining superior quality standards is crucial to meet the expectations of both producers and consumers. Traditional approaches to assessing wine quality…

Abstract

Purpose

In the wine industry, maintaining superior quality standards is crucial to meet the expectations of both producers and consumers. Traditional approaches to assessing wine quality involve labor-intensive processes and rely on the expertise of connoisseurs proficient in identifying taste profiles and key quality factors. In this research, we introduce an innovative and efficient approach centered on the analysis of volatile organic compounds (VOCs) signals using an electronic nose, thereby empowering nonexperts to accurately assess wine quality.

Design/methodology/approach

To devise an optimal algorithm for this purpose, we conducted four computational experiments, culminating in the development of a specialized deep learning network. This network seamlessly integrates 1D-convolutional and long-short-term memory layers, tailor-made for the intricate task at hand. Rigorous validation ensued, employing a leave-one-out cross-validation methodology to scrutinize the efficacy of our design.

Findings

The outcomes of these e-demonstrates were subjected to meticulous evaluation and analysis, which unequivocally demonstrate that our proposed architecture consistently attains promising recognition accuracies, ranging impressively from 87.8% to an astonishing 99.41%. All this is achieved within a remarkably brief timeframe of a mere 4 seconds. These compelling findings have far-reaching implications, promising to revolutionize the assessment and tracking of wine quality, ultimately affording substantial benefits to the wine industry and all its stakeholders, with a particular focus on the critical aspect of VOCs signal analysis.

Originality/value

This research has not been published anywhere else.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

Article
Publication date: 29 March 2024

Pratheek Suresh and Balaji Chakravarthy

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a…

Abstract

Purpose

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

Design/methodology/approach

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R2) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

Findings

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

Research limitations/implications

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

Originality/value

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

Details

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

Keywords

Article
Publication date: 4 March 2024

Betul Gokkaya, Erisa Karafili, Leonardo Aniello and Basel Halak

The purpose of this study is to increase awareness of current supply chain (SC) security-related issues by providing an extensive analysis of existing SC security solutions and…

Abstract

Purpose

The purpose of this study is to increase awareness of current supply chain (SC) security-related issues by providing an extensive analysis of existing SC security solutions and their limitations. The security of SCs has received increasing attention from researchers, due to the emerging risks associated with their distributed nature. The increase in risk in SCs comes from threats that are inherently similar regardless of the type of SC, thus, requiring similar defence mechanisms. Being able to identify the types of threats will help developers to build effective defences.

Design/methodology/approach

In this work, we provide an analysis of the threats, possible attacks and traceability solutions for SCs, and highlight outstanding problems. Through a comprehensive literature review (2015–2021), we analysed various SC security solutions, focussing on tracking solutions. In particular, we focus on three types of SCs: digital, food and pharmaceutical that are considered prime targets for cyberattacks. We introduce a systematic categorization of threats and discuss emerging solutions for prevention and mitigation.

Findings

Our study shows that the current traceability solutions for SC systems do not offer a broadened security analysis and fail to provide extensive protection against cyberattacks. Furthermore, global SCs face common challenges, as there are still unresolved issues, especially those related to the increasing SC complexity and interconnectivity, where cyberattacks are spread across suppliers.

Originality/value

This is the first time that a systematic categorization of general threats for SC is made based on an existing threat model for hardware SC.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

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