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1 – 10 of 13
Article
Publication date: 14 April 2014

Sushant Negi, Suresh Dhiman and Rajesh Kumar Sharma

This study aims to provide an overview of rapid prototyping (RP) and shows the potential of this technology in the field of medicine as reported in various journals and…

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Abstract

Purpose

This study aims to provide an overview of rapid prototyping (RP) and shows the potential of this technology in the field of medicine as reported in various journals and proceedings. This review article also reports three case studies from open literature where RP and associated technology have been successfully implemented in the medical field.

Design/methodology/approach

Key publications from the past two decades have been reviewed.

Findings

This study concludes that use of RP-built medical model facilitates the three-dimensional visualization of anatomical part, improves the quality of preoperative planning and assists in the selection of optimal surgical approach and prosthetic implants. Additionally, this technology makes the previously manual operations much faster, accurate and cheaper. The outcome based on literature review and three case studies strongly suggests that RP technology might become part of a standard protocol in the medical sector in the near future.

Originality/value

The article is beneficial to study the influence of RP and associated technology in the field of medicine.

Details

Rapid Prototyping Journal, vol. 20 no. 3
Type: Research Article
ISSN: 1355-2546

Keywords

Article
Publication date: 13 November 2017

Blaza Stojanovic, Jasmina Blagojevic, Miroslav Babic, Sandra Velickovic and Slavica Miladinovic

This research aims to describe the influence of weight per cent of graphite (Gr), applied load and sliding speed on the wear behavior of aluminum (Al) alloy A356…

Abstract

Purpose

This research aims to describe the influence of weight per cent of graphite (Gr), applied load and sliding speed on the wear behavior of aluminum (Al) alloy A356 reinforced with silicon carbide (SiC) (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) particles. The objective is to analyze the effect of the aforementioned parameters on a specific wear rate.

Design/methodology/approach

These hybrid composites are obtained by means of the compo-casting process. Tribological analyses were conducted on block-on-disc tribometer at three different loads (10, 20 and 30 N) and three different sliding speeds (0.25, 0.5 and 1 m/s), at the sliding distance of 900 m, in dry sliding wear conditions. Optimization of the tribological behavior was conducted via the Taguchi method, and ANOVA was used for the analysis of the specific wear rate. Confirmation tests are used to foresee and check the experimental results. Examined samples were analyzed via a scanning electron microscope (SEM). Regression models for predicting specific wear rate were developed with Taguchi and ANN (artificial neural network) methods.

Findings

The biggest impact on value of specific wear rate has the load (43.006%), while the impact of Wt.% Gr (31.514%) was less. After comparison of the results, i.e. regression models, for predicting the specific wear rate, it was observed that ANN was more efficient than the Taguchi method. The specific wear rate of Al alloy A356 with SiC (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) decreases with a decrease in the load and weight per cent of Gr-reinforcing material, as well as with a decrease in sliding speed.

Originality/value

The results obtained in this paper using the Taguchi method and the ANN method are useful for improving and further investigating the wear behavior of the SiC- and Gr-reinforced Al alloy A356.

Details

Industrial Lubrication and Tribology, vol. 69 no. 6
Type: Research Article
ISSN: 0036-8792

Keywords

Article
Publication date: 3 November 2022

Rajat Yadav, Anas Islam and Vijay Kumar Dwivedi

The purpose of this paper is to study Al-based green composite. To make composite samples of aluminium alloy (AA3105) with different weight percentages of rice husk ash…

Abstract

Purpose

The purpose of this paper is to study Al-based green composite. To make composite samples of aluminium alloy (AA3105) with different weight percentages of rice husk ash (RHA) and eggshell (ES) particles as reinforcement, stir casting method was used.

Design/methodology/approach

Several other aspects, including the weight percent of reinforcing agent particles, the applied stress and the sliding speed, were taken into consideration. During the course of the wear test, the sliding distance that was recorded varied from a minimum of 1,000 m all the way up to a maximum of 3,135 m (10, 15, 20, 25 and 30 min). The typical range for normal loads is 8–24 N, and their speed is 1.58 m/s.

Findings

With the AA/ES/RHA composite, the wear rates decreases when the grain size of the reinforcing particles enhanced. Scanning electron microscopy images of worn surfaces show that at low speeds, delaminating and ploughing are the main causes of wear. At high speeds, ploughing is major cause of wear. Composites with better wear-resistant properties can be used in wide range of tribological applications, especially in the automotive industry. It was found that hardness increases at the same time as the weight of the reinforcement increases. Tensile and hardness were maximized at 10% reinforcement mix in Al3105.

Originality/value

In this work, ES and RHA has been used to develop green metal matrix composite to support green revolution as promoted/suggested by United Nations thus reducing the environmental pollution.

Details

World Journal of Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1708-5284

Keywords

Content available
Article
Publication date: 9 August 2021

Shilpa Sharma, Punam Rattan, Anurag Sharma and Mohammad Shabaz

This paper aims to introduce recently an unregulated unsupervised algorithm focused on voice activity detection by data clustering maximum margin, i.e. support vector…

Abstract

Purpose

This paper aims to introduce recently an unregulated unsupervised algorithm focused on voice activity detection by data clustering maximum margin, i.e. support vector machine. The algorithm for clustering K-mean used to solve speech behaviour detection issues was later applied, the application, therefore, did not permit the identification of voice detection. This is critical in demands for speech recognition.

Design/methodology/approach

Here, the authors find a voice activity detection detector based on a report provided by a K-mean algorithm that permits sliding window detection of voice and noise. However, first, it needs an initial detection pause. The machine initialized by the algorithm will work on health-care infrastructure and provides a platform for health-care professionals to detect the clear voice of patients.

Findings

Timely usage discussion on many histories of NOISEX-92 var reveals the average non-speech and the average signal-to-noise ratios hit concentrations which are higher than modern voice activity detection.

Originality/value

Research work is original.

Details

World Journal of Engineering, vol. 19 no. 1
Type: Research Article
ISSN: 1708-5284

Keywords

Content available
Article
Publication date: 8 June 2021

Jyoti Godara, Rajni Aron and Mohammad Shabaz

Sentiment analysis has observed a nascent interest over the past decade in the field of social media analytics. With major advances in the volume, rationality and veracity…

Abstract

Purpose

Sentiment analysis has observed a nascent interest over the past decade in the field of social media analytics. With major advances in the volume, rationality and veracity of social networking data, the misunderstanding, uncertainty and inaccuracy within the data have multiplied. In the textual data, the location of sarcasm is a challenging task. It is a different way of expressing sentiments, in which people write or says something different than what they actually intended to. So, the researchers are showing interest to develop various techniques for the detection of sarcasm in the texts to boost the performance of sentiment analysis. This paper aims to overview the sentiment analysis, sarcasm and related work for sarcasm detection. Further, this paper provides training to health-care professionals to make the decision on the patient’s sentiments.

Design/methodology/approach

This paper has compared the performance of five different classifiers – support vector machine, naïve Bayes classifier, decision tree classifier, AdaBoost classifier and K-nearest neighbour on the Twitter data set.

Findings

This paper has observed that naïve Bayes has performed the best having the highest accuracy of 61.18%, and decision tree performed the worst with an accuracy of 54.27%. Accuracy of AdaBoost, K-nearest neighbour and support vector machine measured were 56.13%, 54.81% and 59.55%, respectively.

Originality/value

This research work is original.

Details

World Journal of Engineering, vol. 19 no. 1
Type: Research Article
ISSN: 1708-5284

Keywords

Article
Publication date: 9 February 2015

C.S. Devaki, D. D. Wadikar and P.E. Patki

The purpose of the paper was to assess the functional properties vegetable gourds & the validated health claims so as to help the future researchers to locate the gaps…

Abstract

Purpose

The purpose of the paper was to assess the functional properties vegetable gourds & the validated health claims so as to help the future researchers to locate the gaps. However, emphasizing on the scientifically available reports was required to make information available in a nutshell to the health-conscious consumers, as well as the researcher from the area of functional foods and nutrition.

Design/methodology/approach

The paper is a mini-review of scientific findings in different studies on gourd vegetables. The approach to information collection was finding the research gaps and potential areas for future work with a nutritional perspective.

Findings

Ash gourd, bitter gourd and bottle gourd have been extensively studied, and several health benefits and functional components have been reported, while ridge gourd, snake gourd and pointed gourd have been sparsely studied for their therapeutic benefits and the validation thereof; hence, there lies a scope for researchers.

Research limitations/implications

The scarcity of scientific reports compared to the traditional usage and folkloric beliefs was a limitation.

Originality/value

Understanding the nutritional potential of gourd vegetables from scientific reports may influence both the work areas and consumers in the appropriate direction.

Details

Nutrition & Food Science, vol. 45 no. 1
Type: Research Article
ISSN: 0034-6659

Keywords

Article
Publication date: 10 May 2021

Ravi Butola, N. Yuvaraj, Ravi Pratap Singh, Lakshay Tyagi and Faim Khan

This study aims to analyse the changes in mechanical and wear performance of aluminium alloy when yttrium oxide particles are incorporated. The microstructures are studied…

Abstract

Purpose

This study aims to analyse the changes in mechanical and wear performance of aluminium alloy when yttrium oxide particles are incorporated. The microstructures are studied to analyse the change in the grain structures. Worn surfaces are observed via scanning electron microscope to study the wear mechanism in detail.

Design/methodology/approach

Stir casting is used to incorporate varying composition of yttrium particles, having an average particle size of 25 micrometer, in aluminium alloy 6063 matrix. Wear testing is carried out by DUCOM manufactured high temperature rotatory tribometer, and an indentation test is used for analysing the microhardness of the fabricated samples.

Findings

Microhardness of the material is increased with the increasing content of particulate addition. With the increasing content of reinforcement, more refined grains are produced. The load is transferred from the matrix to more rigid yttrium oxide particles. These factors contributed to escalated microhardness of the reinforced samples. Particulate addition enhanced the wear performance of the material; this might be attributed to increased microhardness and formation of an oxide layer.

Originality/value

Aluminium composites are finding wide applications in various industries, and there is always a requirement of material with enhanced tribological properties. Yttrium oxide particles exhibit improved mechanical properties, and their interaction with the aluminium matrix has not been studied much in the past. So, in this work, yttrium oxide incorporated aluminium matrix is studied.

Details

World Journal of Engineering, vol. 19 no. 3
Type: Research Article
ISSN: 1708-5284

Keywords

Article
Publication date: 30 November 2021

Oluwafemi Ajayi and Reolyn Heymann

Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly…

Abstract

Purpose

Energy management is critical to data centres (DCs) majorly because they are high energy-consuming facilities and demand for their services continue to rise due to rapidly increasing global demand for cloud services and other technological services. This projected sectoral growth is expected to translate into increased energy demand from the sector, which is already considered a major energy consumer unless innovative steps are used to drive effective energy management systems. The purpose of this study is to provide insights into the expected energy demand of the DC and the impact each measured parameter has on the building's energy demand profile. This serves as a basis for the design of an effective energy management system.

Design/methodology/approach

This study proposes novel tunicate swarm algorithm (TSA) for training an artificial neural network model used for predicting the energy demand of a DC. The objective is to find the optimal weights and biases of the model while avoiding commonly faced challenges when using the backpropagation algorithm. The model implementation is based on historical energy consumption data of an anonymous DC operator in Cape Town, South Africa. The data set provided consists of variables such as ambient temperature, ambient relative humidity, chiller output temperature and computer room air conditioning air supply temperature, which serve as inputs to the neural network that is designed to predict the DC’s hourly energy consumption for July 2020. Upon preprocessing of the data set, total sample number for each represented variable was 464. The 80:20 splitting ratio was used to divide the data set into training and testing set respectively, making 452 samples for the training set and 112 samples for the testing set. A weights-based approach has also been used to analyze the relative impact of the model’s input parameters on the DC’s energy demand pattern.

Findings

The performance of the proposed model has been compared with those of neural network models trained using state of the art algorithms such as moth flame optimization, whale optimization algorithm and ant lion optimizer. From analysis, it was found that the proposed TSA outperformed the other methods in training the model based on their mean squared error, root mean squared error, mean absolute error, mean absolute percentage error and prediction accuracy. Analyzing the relative percentage contribution of the model's input parameters based on the weights of the neural network also shows that the ambient temperature of the DC has the highest impact on the building’s energy demand pattern.

Research limitations/implications

The proposed novel model can be applied to solving other complex engineering problems such as regression and classification. The methodology for optimizing the multi-layered perceptron neural network can also be further applied to other forms of neural networks for improved performance.

Practical implications

Based on the forecasted energy demand of the DC and an understanding of how the input parameters impact the building's energy demand pattern, neural networks can be deployed to optimize the cooling systems of the DC for reduced energy cost.

Originality/value

The use of TSA for optimizing the weights and biases of a neural network is a novel study. The application context of this study which is DCs is quite untapped in the literature, leaving many gaps for further research. The proposed prediction model can be further applied to other regression tasks and classification tasks. Another contribution of this study is the analysis of the neural network's input parameters, which provides insight into the level to which each parameter influences the DC’s energy demand profile.

Details

Journal of Engineering, Design and Technology , vol. 20 no. 5
Type: Research Article
ISSN: 1726-0531

Keywords

Article
Publication date: 1 September 2020

Ashutosh Muduli and Gary N. McLean

Benchmarking research has explored the role of organizational practices and business processes rooted with human capabilities for achieving growth performance. The role of…

Abstract

Purpose

Benchmarking research has explored the role of organizational practices and business processes rooted with human capabilities for achieving growth performance. The role of high performance work system as an organizational practice and business process is yet to be studied. Even if studied, no study has been conducted on the role of training transfer climate on high performance work system and organizational performance. The current research aims at examining high performance work system on organizational performance. Further, the study also examine training transfer climate as a mediating variable between HPWS and organizational performance.

Design/methodology/approach

Data collected from 415 executives of a high performance-based power sector company of Gujarat, India. The survey instrument consists of high performance work system, training transfer climate and organizational performance. Confirmatory factor analysis was used for a simultaneous assessment of overall and specific elements of measurement validity and reliability. Structural equation modelling used to test the hypothesized model.

Findings

The result proved the capability of high performance work system to predict organizational performance. Further, the result supports the hypothesis that training transfer climate acts as a mediator between high performance work system and organizational performance.

Research limitations/implications

The result has important theoretical and managerial implications. Theoretically, the research extends the scope of benchmarking to high performance work system. The managerial implications have been discussed from the training transfer climate perspectives.

Originality/value

The originality of the study lies with proving the role of high performance work system and training transfer climate as an organizational practice and business process within benchmarking research.

Details

Benchmarking: An International Journal, vol. 28 no. 1
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 26 May 2022

Amit Kumar Yadav and Dinesh Kumar

Each individual needs to be vaccinated to control the spread of the COVID-19 pandemic in the shortest possible time. However, the vaccine distribution with an already…

Abstract

Purpose

Each individual needs to be vaccinated to control the spread of the COVID-19 pandemic in the shortest possible time. However, the vaccine distribution with an already strained supply chain in low- and middle-income countries (LMICs) will not be effective enough to vaccinate all the population in stipulated time. The purpose of this paper is to show that there is a need to revolutionize the vaccine supply chain (VSC) by overcoming the challenges of sustainable vaccine distribution.

Design/methodology/approach

An integrated lean, agile and green (LAG) framework is proposed to overcome the challenges of the sustainable vaccine supply chain (SVSC). A hybrid best worst method (BWM)–Measurement of Alternatives and Ranking According to COmpromise Solution (MARCOS) methodology is designed to analyze the challenges and solutions.

Findings

The analysis shows that vaccine wastage is the most critical challenge for SVSC, and the coordination among stakeholders is the most significant solution followed by effective management support.

Social implications

The result of the analysis can help the health care organizations (HCOs) to manage the VSC. The effective vaccination in stipulated time will help control the further spread of the virus, which will result in the normalcy of business and availability of livelihood for millions of people.

Originality/value

To the best of the author's knowledge, this is the first study to explore sustainability in VSC by considering the environmental and social impact of vaccination. The LAG-based framework is also a new approach in VSC to find the solution for existing challenges.

Details

Journal of Humanitarian Logistics and Supply Chain Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2042-6747

Keywords

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